{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c7662b72",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-16T03:10:42.569793Z",
     "start_time": "2023-04-16T03:10:41.060396Z"
    }
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "from torch import nn\n",
    "import transformers\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1c3b6da3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-16T03:10:44.059990Z",
     "start_time": "2023-04-16T03:10:44.054939Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.0.0+cu118\n",
      "4.28.0.dev0\n"
     ]
    }
   ],
   "source": [
    "print(torch.__version__)\n",
    "print(transformers.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b8c744d9",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-16T03:11:02.200638Z",
     "start_time": "2023-04-16T03:11:02.193759Z"
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib as mpl\n",
    "# default: 100\n",
    "mpl.rcParams['figure.dpi'] = 200"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45c148ff",
   "metadata": {},
   "source": [
    "# text classification"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ceade67b",
   "metadata": {},
   "source": [
    "- 也叫 sequence classification\n",
    "- sentiment analysis\n",
    "    - 情感分析，就是一种文本/序列分类\n",
    "        - 电商评论\n",
    "        - social web：weibo/tweet"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66fc6459",
   "metadata": {},
   "source": [
    "## emotions 数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3abd91ab",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-16T03:11:57.058240Z",
     "start_time": "2023-04-16T03:11:56.718499Z"
    }
   },
   "outputs": [],
   "source": [
    "from datasets import load_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5fef5dac",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-16T03:12:03.105243Z",
     "start_time": "2023-04-16T03:11:59.563901Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No config specified, defaulting to: emotion/split\n",
      "Found cached dataset emotion (/home/whaow/.cache/huggingface/datasets/emotion/split/1.0.0/cca5efe2dfeb58c1d098e0f9eeb200e9927d889b5a03c67097275dfb5fe463bd)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "37abdaccea7d454d84e6b2f5e67bcd1d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/3 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "emotions = load_dataset('emotion')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1c02d4aa",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-16T03:12:07.424555Z",
     "start_time": "2023-04-16T03:12:07.413456Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatasetDict({\n",
       "    train: Dataset({\n",
       "        features: ['text', 'label'],\n",
       "        num_rows: 16000\n",
       "    })\n",
       "    validation: Dataset({\n",
       "        features: ['text', 'label'],\n",
       "        num_rows: 2000\n",
       "    })\n",
       "    test: Dataset({\n",
       "        features: ['text', 'label'],\n",
       "        num_rows: 2000\n",
       "    })\n",
       "})"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# DatasetDict\n",
    "# 8:1:1\n",
    "emotions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "45bbd973",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-16T03:12:39.288495Z",
     "start_time": "2023-04-16T03:12:39.278279Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['train', 'validation', 'test'])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emotions.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cbedfd63",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-16T03:12:49.433763Z",
     "start_time": "2023-04-16T03:12:49.362208Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset({\n",
      "    features: ['text', 'label'],\n",
      "    num_rows: 16000\n",
      "}) <class 'datasets.arrow_dataset.Dataset'>\n",
      "['i didnt feel humiliated', 'i can go from feeling so hopeless to so damned hopeful just from being around someone who cares and is awake', 'im grabbing a minute to post i feel greedy wrong', 'i am ever feeling nostalgic about the fireplace i will know that it is still on the property', 'i am feeling grouchy']\n",
      "[0, 0, 3, 2, 3]\n",
      "{'text': ['i didnt feel humiliated', 'i can go from feeling so hopeless to so damned hopeful just from being around someone who cares and is awake', 'im grabbing a minute to post i feel greedy wrong', 'i am ever feeling nostalgic about the fireplace i will know that it is still on the property', 'i am feeling grouchy'], 'label': [0, 0, 3, 2, 3]}\n"
     ]
    }
   ],
   "source": [
    "print(emotions['train'], type(emotions['train']))\n",
    "# 继续支持key\n",
    "print(emotions['train']['text'][:5])\n",
    "print(emotions['train']['label'][:5])\n",
    "# 支持 index\n",
    "print(emotions['train'][:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a0874919",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-16T03:15:03.962063Z",
     "start_time": "2023-04-16T03:15:03.953895Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'text': Value(dtype='string', id=None), 'label': ClassLabel(names=['sadness', 'joy', 'love', 'anger', 'fear', 'surprise'], id=None)}\n",
      "ClassLabel(names=['sadness', 'joy', 'love', 'anger', 'fear', 'surprise'], id=None)\n",
      "anger\n"
     ]
    }
   ],
   "source": [
    "print(emotions['train'].features)\n",
    "print(emotions['train'].features['label'])\n",
    "print(emotions['train'].features['label'].int2str(3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e700ba91",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-16T03:15:51.479891Z",
     "start_time": "2023-04-16T03:15:51.468396Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['sadness', 'joy', 'love', 'anger', 'fear', 'surprise']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels = emotions['train'].features['label'].names\n",
    "print(labels)\n",
    "# 下游任务（downstream task）\n",
    "num_classes = len(labels)\n",
    "num_classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c4b1211c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-16T03:16:14.434552Z",
     "start_time": "2023-04-16T03:16:14.428063Z"
    }
   },
   "outputs": [],
   "source": [
    "def int2str(x):\n",
    "#     return emotions['train'].features['label'].int2str(x)\n",
    "    return labels[x]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3faf41e8",
   "metadata": {},
   "source": [
    "## data visualization analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7b75071",
   "metadata": {},
   "source": [
    "- dataset => dataframe\n",
    "- text length \n",
    "- label freq"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00b51930",
   "metadata": {},
   "source": [
    "### dataset to dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a40d028e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-16T03:16:44.573084Z",
     "start_time": "2023-04-16T03:16:44.128889Z"
    }
   },
   "outputs": [],
   "source": [
    "emotions_df = pd.DataFrame.from_dict(emotions['train'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "48417009",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-16T03:16:53.933414Z",
     "start_time": "2023-04-16T03:16:53.922881Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(16000, 2) Index(['text', 'label'], dtype='object')\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>i didnt feel humiliated</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>i can go from feeling so hopeless to so damned...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>im grabbing a minute to post i feel greedy wrong</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>i am ever feeling nostalgic about the fireplac...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>i am feeling grouchy</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                text  label\n",
       "0                            i didnt feel humiliated      0\n",
       "1  i can go from feeling so hopeless to so damned...      0\n",
       "2   im grabbing a minute to post i feel greedy wrong      3\n",
       "3  i am ever feeling nostalgic about the fireplac...      2\n",
       "4                               i am feeling grouchy      3"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(emotions_df.shape, emotions_df.columns)\n",
    "emotions_df[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "298c1fbf",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-16T03:17:31.036831Z",
     "start_time": "2023-04-16T03:17:31.021420Z"
    }
   },
   "outputs": [],
   "source": [
    "# emotions_df['label_name'] = emotions_df['label'].apply(lambda x: emotions['train'].features['label'].int2str(x))\n",
    "emotions_df['label_name'] = emotions_df['label'].apply(lambda x: labels[x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "64d2f45c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-16T03:17:39.416718Z",
     "start_time": "2023-04-16T03:17:39.399554Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>label</th>\n",
       "      <th>label_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>i didnt feel humiliated</td>\n",
       "      <td>0</td>\n",
       "      <td>sadness</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>i can go from feeling so hopeless to so damned...</td>\n",
       "      <td>0</td>\n",
       "      <td>sadness</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>im grabbing a minute to post i feel greedy wrong</td>\n",
       "      <td>3</td>\n",
       "      <td>anger</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>i am ever feeling nostalgic about the fireplac...</td>\n",
       "      <td>2</td>\n",
       "      <td>love</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>i am feeling grouchy</td>\n",
       "      <td>3</td>\n",
       "      <td>anger</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                text  label label_name\n",
       "0                            i didnt feel humiliated      0    sadness\n",
       "1  i can go from feeling so hopeless to so damned...      0    sadness\n",
       "2   im grabbing a minute to post i feel greedy wrong      3      anger\n",
       "3  i am ever feeling nostalgic about the fireplac...      2       love\n",
       "4                               i am feeling grouchy      3      anger"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emotions_df[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17047c0f",
   "metadata": {},
   "source": [
    "### label analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "331aa7d9",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-16T03:17:57.314838Z",
     "start_time": "2023-04-16T03:17:57.301124Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    5362\n",
       "0    4666\n",
       "3    2159\n",
       "4    1937\n",
       "2    1304\n",
       "5     572\n",
       "Name: label, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emotions_df.label.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8680e152",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-16T03:18:24.250409Z",
     "start_time": "2023-04-16T03:18:24.235434Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "joy         5362\n",
       "sadness     4666\n",
       "anger       2159\n",
       "fear        1937\n",
       "love        1304\n",
       "surprise     572\n",
       "Name: label_name, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emotions_df.label_name.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d3270335",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-16T03:18:39.893017Z",
     "start_time": "2023-04-16T03:18:39.634638Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'freq of labels')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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27DH/2l2vXr1M+xs8eLB+++03LVmyRJL00UcfKTg4WC1btlThwoV19OhRbdy4UcnJyWrQoIGqVKliPsTNCh999JG2b9+ubdu2KTk5WSNGjNCkSZPUpEkTeXl56fDhw9q0aZP5wDpXV1fNnj1bFSpUsGwMVhk4cKAmT56sv//+W4mJierXr5/ee+89/etf/5Knp6f27t1rPr37X//6l9q1a6cPPvgg2/0/99xzWrp0qVavXq0KFSqoRYsWKlu2rM6fP6/169enO8WtR48eevrpp+32ZfXPUVZSUlIUEhKikJAQubm5qVatWqpataoKFy6sf/75R8eOHVNYWJgZLl1cXDR16tRb2heA20fAAID7iIeHh0JCQvT222/r448/Vnx8vJKSkvT777/bvWjYzc1NjRo1stvnggULNGjQIDM4xMbGavHixenqBAYGasmSJXr99detOxjdeLbDunXr9PTTT2vRokWSblxr8P3332eoW7JkSc2ePVvt27e3dAxW8fDw0LJly9S+fXvz4uQDBw7owIED6eo1btxYCxcu1MyZM3PUf+HChRUSEqKgoCD9+eefWrlyZab1+vXrp9mzZ2c5Vqt/jhxJe7vca9euadeuXXZvkVusWDHNmDEjTz6lHbhfEDAA4D6TemrTyJEj9fXXX2vNmjXav3+/YmNjde3aNXl7e6tcuXKqXbu2WrZsqQ4dOjg8l93d3V3fffed+vfvr5kzZyo0NFSxsbEqUqSIHnjgAT311FMaMGCApadHpeXl5aWFCxdq1KhR+uabb7RhwwadOnVKV69eVdGiRVWrVi116tRJgwcPtuxBf7mlatWq2rVrl6ZNm6YlS5bor7/+UlJSkkqUKKHatWvrqaee0hNPPJHpqUvZUa1aNW3btk1z5szRokWLdOjQIV24cEHFihXTI488oqFDh6p169bZ6svqnyNHfvnlF+3atUtr167V//73Px04cEBRUVGKi4uTh4eH/P399eCDD6pDhw566qmn5O3tfUv7AWANm5Gde9YBAGCBgQMHat68eZKkuXPnauDAgc4dEADAclzkDQAAAMAyBAwAAAAAliFgAAAAALAMAQMAAACAZQgYAAAAACxDwAAAAABgGW5TCwAAAMAyrGAAAAAAsAwBAwAAAIBlCBgAAAAALEPAAAAAAGAZAgYAAAAAyxAwAAAAAFiGgAEAAADAMq7OHgDuTQkJCdqzZ48kyd/fX66u/KgBAADkJcnJyYqJiZEk1a5dW56enpb0y6c+5Io9e/YoMDDQ2cMAAABANoSHh6tBgwaW9MUpUgAAAAAswwoGcoW/v7+5HR4erpIlSzpxNAAAALhZdHS0ecZJ2s9ut4uAgVyR9pqLkiVLqkyZMk4cDQAAAByx8npZTpECAAAAYBkCBgAAAADLEDAAAAAAWIaAAQAAAMAyBAwAAAAAliFgAAAAALAMAQMAAACAZQgYAAAAACxDwAAAAABgGQIGAAAAAMsQMAAAAABYhoABAAAAwDIEDAAAAACWIWAAAAAAsAwBAwAAAIBlCBgAAAAALOPq7AHg3tfovbVy9S7q7GEAAADkeZGTOjp7CLeNFQwAAAAAliFgAAAAALAMAQMAAACAZQgYAAAAACxDwAAAAABgGQIGAAAAAMsQMAAAAABYhoDhJMHBwbLZbLLZbIqMjHT2cAAAAABLEDAAAAAAWIaAAQAAAMAyBAwnGThwoAzDkGEYKl++vLOHAwAAAFiCgAEAAADAMgQMAAAAAJYhYDhJdu4iFRMTozfeeEN169aVj4+PPD09Vb58efXr109btmzJtM0vv/xi9rtw4cIsx/HSSy/JZrPJxcVFJ06cuJ1DAgAAAAgYedWqVatUuXJlTZgwQREREbp06ZISExN17Ngxffvtt2ratKlGjBihlJSUdO06duyoUqVKSZLmzp3rcB/Jycn69ttvJUmPPvqoAgICcudgAAAAcN8gYORBERER6ty5sy5fviw3NzeNGjVK69evV3h4uL766itVqFBBkjRt2jSNHj06XVsXFxcNGDBAkrR69WpFRUXZ3c9vv/2ms2fPSpIGDx6cS0cDAACA+wkBIw8aOnSokpKS5OLiol9//VVTpkxRixYt1KBBAw0dOlQ7duxQjRo1JEkfffSR9u3bl679kCFDZLPZlJKSoq+//trufubMmSNJKlKkiLp27ZqjMUZFRTn8io6OzuFRAwAA4F5AwMhjwsPDtW3bNkk3gkLbtm0z1PH19dWMGTMkSSkpKZo+fXq68ooVK6ply5aSblzrkZmzZ88qJCREktSnTx95eHjkaJwBAQEOvwIDA3PUHwAAAO4NBIw8Zs2aNeb2008/bbde48aNVb169QxtUg0ZMkSSdPDgwUwvCP/mm2+UnJwsidOjAAAAYB1XZw8A6e3du1eS5O7urrp16zqs27BhQx04cEAHDx5UUlKS3N3dzbLu3bvLz89P58+f19y5c9WkSZN0bVMvAH/ooYf0r3/9K8fjzOqOU9HR0axiAAAA3IdYwchjzp8/L0ny8/OTq6vj/FeiRAlJkmEYunDhQroyDw8P9evXT5K0aNEixcXFmWXh4eHmdRuOVkkcKVOmjMOvkiVL3lK/AAAAuLsRMPIom82WZR3DMByWp54mdeXKFf3www/m66mrF56ennrqqaduY5QAAABAegSMPMbPz0+SdO7cOfMaCXvOnDkj6UYY8fX1zVBeq1YtNWzYUNL/hYqEhAR9//33kqSgoCD5+PhYNXQAAACAgJHX1KpVS5KUlJSkXbt2OawbHh4uSapSpUq66y/SSl3F2LRpk44cOaIlS5bo4sWLkri4GwAAANYjYOQxbdq0Mbdnz55tt15oaKj279+foc3NevXqJS8vLxmGoeDgYHMlo3z58mrdurVFowYAAABuIGDkMYGBgWrQoIEkadasWVq9enWGOpcuXdKwYcMkSfny5dPw4cPt9ufl5aVevXpJkr766iutW7dOkjRw4MBsXecBAAAA5AQBIw+aMWOG3N3ddf36dXXs2FEvvfSSNmzYoO3bt2vmzJl66KGHtGfPHknSyy+/bJ5WZU/qaVJnz55VSkqKbDabBg4cmNuHAQAAgPsQz8HIg+rUqaNly5bpiSee0OXLlzV58mRNnjw5Q73nn39eEydOzLK/hg0bqnbt2mYoadOmjcqVK2f5uAEAAABWMPKotm3b6tChQxozZozq1Kkjb29veXh4qGzZsurTp482b96szz//XPnyZW8K+/bta25zcTcAAAByCysYTpKSkmJu23ugnr+/vyZMmKAJEybc9v5SVy98fX0VFBR02/0BAAAAmWEFw0kuX75sbhcuXDhX93Xp0iX9+OOPkqQ+ffrIw8MjV/cHAACA+xcBw0kiIiIkSSVKlFChQoVydV/Tpk3T1atXJcm8+xQAAACQGzhF6g46e/asjhw5og0bNmjBggWSpPbt21u+n+TkZEVGRioxMVGbNm3Su+++K0nq1KlTlnecAgAAAG4HAeMOmj59ut5++23zex8fH73xxhuW7ycqKkpVqlRJ95q3t7emTJli+b4AAACAtDhF6g7Lly+fihcvrl69eik0NFQVK1bM1f0VK1ZMXbt21datW1W5cuVc3RcAAADACsYdNG7cOI0bNy7X91O+fHkZhpHr+wEAAABuxgoGAAAAAMuwgoFcFzamtcqUKePsYQAAAOAOYAUDAAAAgGUIGAAAAAAsQ8AAAAAAYBkCBgAAAADLEDAAAAAAWIaAAQAAAMAyBAwAAAAAliFgAAAAALAMAQMAAACAZQgYAAAAACxDwAAAAABgGQIGAAAAAMsQMAAAAABYhoABAAAAwDIEDAAAAACWIWAAAAAAsAwBAwAAAIBlCBgAAAAALEPAAAAAAGAZAgYAAAAAyxAwAAAAAFiGgAEAAADAMgQMAAAAAJYhYAAAAACwDAEDAAAAgGUIGAAAAAAsQ8AAAAAAYBkCBgAAAADLEDAAAAAAWIaAAQAAAMAyBAwAAAAAliFgAAAAALAMAQMAAACAZQgYAAAAACxDwAAAAABgGQIGAAAAAMsQMAAAAABYhoABAAAAwDKuzh4A7n2N3lsrV++izh4GAAC4wyIndXT2EOAErGAAAAAAsAwBAwAAAIBlCBgAAAAALEPAAAAAAGAZAgYAAAAAyxAwAAAAAFiGgAEAAADAMvd9wChfvrxsNpsGDhzo7KEAAAAAd737PmAAAAAAsA4BAwAAAIBlCBgAAAAALEPAAAAAAGAZAgYAAAAAy+R6wDh16pRee+01PfTQQypcuLDc3d1VokQJ1a5dW71791ZwcLAuX76crk1cXJwWLlyoIUOGqE6dOipcuLDc3Nzk7++v5s2b66OPPtKVK1eytf+QkBC1b99e/v7+KlCggKpWraoXX3xRp06dyrJtcHCwbDabbDabIiMjlZKSohkzZuiRRx6Rr6+vChYsqAcffFATJkxQfHx8lv0ZhqEffvhBPXr0UEBAgDw9PeXr66vAwECNHz9eFy9edNj+77//1siRI1WrVi15eXnJ3d1dpUqVUp06dTR48GAtXLhQiYmJGdpdv35dwcHBateunUqUKCF3d3f5+PioSpUqat26td577z3t378/y/EDAAAAWbEZhmHkVuebN29Wp06dMgSImy1btkydOnUyv2/RooU2btzosE2FChUUEhKiatWq2a0zatQoffrpp5mWFStWTCEhIerRo4eOHTumAQMGKDg4OF2d4OBgDRo0SJK0d+9evfDCC1q3bl2m/QUGBmrdunUqWLBgpuUxMTEKCgrS77//bne8xYsX19KlS9WwYcMMZYsXL1bfvn2VlJRkt70k7dmzR7Vq1TK/v3Llijp06KDNmzc7bNejRw/98MMPDuvkRFRUlAICAiRJpYcHy9W7qGV9AwCAu0PkpI7OHgIcSPt57cSJEypTpowl/bpa0ksmEhMT1atXL12+fFmFChXS8OHD1bJlSxUrVkzXrl3TsWPHFBoaqh9//DFD2+TkZNWuXVtdunRR/fr1VapUKRmGoWPHjumnn37SokWLdPToUXXr1k0RERHy9PTM0MfHH39shotSpUpp9OjRCgwMVEJCgn777Td98sknevzxx7O18iBJQ4cOVVhYmAYMGKCePXuqRIkSOn78uD744AOFhoYqPDxc7777riZOnJihbVxcnJo3b64DBw7I3d1dgwYNUocOHRQQEKC4uDht2rRJkydP1pkzZ9S+fXvt2rVL5cqVM9ufOXNGgwYNUlJSkooVK6YRI0aoUaNGKlq0qBISEnTkyBFt2rRJS5YsybDvcePGmeGiU6dO6tOnj8qWLStPT0/FxMRo9+7d+vXXX2Wz2bL1PgAAAACO5NoKxrp169S6dWtJGVco0kpOTlZ8fLy8vb3N1w4ePKgqVarY7XvNmjVq166dUlJSNGvWLD399NPpys+cOaOKFSsqPj5e5cqVU1hYmEqUKJFhfO3atVNycrIkZbmCIUnffPON+vbtm65OYmKi6tevr71796pIkSI6ffq0XF3T57aRI0fq888/V+HChbVmzRrVr18/wzEdO3ZMDz/8sKKjo9W3b1998803ZtmcOXPMY7x5hSKthIQEGYah/Pnzm6+VLVtWJ06c0OOPP67Fixdn2k6Szp8/Lz8/P7vlN4uKinJYHh0drcDAQEmsYAAAcL9iBSNvy60VjFy7BuP06dPmdrNmzezWc3V1TRcuJDkMF5LUpk0bdenSRZL0888/ZyifN2+euTLx8ccfZwgXktSqVSs988wzDveTVvfu3TOEC0ny8PDQiBEjJEnnzp3LcC1DbGysZs2aJUl65513Mg0XklSuXDm9+eabkqSFCxemW1lJfS99fX3thgtJ8vT0TBcu0rZt2rSpw+PLSbiQpICAAIdfqeECAAAA95dcCxglS5Y0t+fOnXtbfcXExOjgwYPau3ev+eXv7y9J2r17d4b6a9askXTjA3nXrl3t9jt48OBsj6FPnz52y+rVq2duHzlyJF3ZypUrlZCQIEnq2bOnw32kBrFr165px44d5uup7+WFCxe0dOnSbI85bdubQwsAAACQG3LtGowmTZqoYsWKOnLkiEaNGqXvvvtOQUFBat68uerXry93d3eH7X///XdNnTpVa9as0fnz5+3Wi42NzfDanj17JEl169bNcLpSWnXq1JG7u3uWF05Lcngxedq//v/zzz/pyrZv325upw1dWUm7AtSlSxf5+Pjo4sWLCgoKUosWLdS5c2c1a9ZMderUkYuLi91+BgwYoPHjx2vr1q2qUKGCnnjiCbVu3VpNmjQxQ9qtOHHihMPytKdIAQAA4P6RawHDzc1Ny5Yt0+OPP64DBw5o27Zt2rZtmyQpf/78at68ufr166cnn3wywwfkcePG6e23387Wfq5evZrhtQsXLki6cacoR1xdXeXn55fuw7w9BQoUsFuWL9//LQRdv349XdnZs2ez7DszaVcbihQpol9++UW9e/fWyZMntX79eq1fv16S5O3trTZt2mjQoEGZXufy5ptv6uTJk5o7d67Onj2radOmadq0abLZbKpZs6a6d++u5557TsWLF8/R+Kw6Rw8AAAD3llx9DkaNGjW0Z88e/fTTTxo8eLAqVaok6UYoWLFihfr06aOGDRum+xC+du1aM1xUrFhR06dP1x9//KGLFy8qOTlZhmHIMAzzegVHsnNnpFy8S6+k/wsc7u7u2rNnT7a/bj61q2nTpjp06JC+/fZbPfXUU+YH/MuXL2vJkiXq3LmzHnvssQynQbm5uWn27Nnau3ev3njjDT3yyCNyd3eXYRjau3ev3nnnHVWuXDnHp14BAAAAmcm1FYxULi4u6tatm7p16ybpxqkzy5cv1/Tp07Vjxw7t2LFDw4YN008//SRJmjlzpiTJx8dHoaGhdlchUlcpMuPr66vTp0/rzJkzDseWnJzssB8rFClSRJKUlJSkIkWK5Og0qZt5enqqT58+5vUgR44c0W+//abPP/9cf//9t1auXKnXX39dU6ZMydC2Ro0aGj9+vMaPH6+rV6/q999/1/z58/X111/rypUr6t27tw4fPnxb4wMAAABy/UneNytZsqQGDx6s0NBQPfTQQ5KkX3/91TzVad++fZJu3OXJ0SlOaa9tuFnt2rUlSREREeZtaDOze/fubF1/cTvq1q1rbq9atcrSvitWrKiRI0dq27Zt5orGokWLsmyXP39+tWnTRnPmzNGHH34o6caq0q+//mrp+AAAAHD/ueMBI5Wbm5uaN28u6cZKwsWLF81tSQ7veBQREaGwsDC75W3atJF049kOy5Yts1tvzpw5OR12jrVv315ubm6SpClTpjgMPLfK29tbDRo0kJT5Re+OpD6r5FbaAgAAADfLtYCxefNmHTp0yG55UlKSNm7cKEny8vIy72iU+gyMLVu2ZLjlq3TjlrWZPY8irQEDBpjPg3jxxRczPVVq48aNmjFjRvYO5jaULl3afFjf7t27NWzYMIch4+zZs+ZzM1KtXLlS0dHRdttcunRJ4eHhkqQKFSqYr58/f16//PKLw+tM0q6qpG0LAAAA3IpcuwZj7dq1Gj9+vJo2baqOHTvqwQcflL+/v65evaq///5bX375pXbu3ClJGjJkiHk72f79+2vZsmW6cuWKmjdvrldffVX16tWTYRjaunWrJk+erNOnT+vhhx9WaGhopvsuXry4xo8fr5dfflmRkZGqV6+eRo8ercDAQCUkJCgkJERTpkxR6dKlFR8fr5iYmNx6GyTdeNjf1q1btXfvXs2ZM0dhYWEaOnSo6tWrJy8vL128eFH79u3TmjVrFBISotq1a2vIkCFm+wULFqhz58569NFH1bZtW9WqVUt+fn76559/tHfvXn3++ec6efKkJGn48OFmu8uXL6tr164qX768unfvroYNG6pcuXJydXVVdHS0li1bZoaZMmXKqHPnzrn6PgAAAODel6sXeaekpGjjxo3mSkVmunfvrokTJ5rfP/744xo0aJDmzp2rqKgojRw5Ml19FxcXTZkyRRcuXLAbMCTppZde0vHjxzV16lSdPHnSfNp2qqJFi+qHH37Q448/fotHl31eXl7auHGj+vTpoxUrVmj//v0aNWqU3fo3P9lcuvHwvZCQEIWEhNht9/zzz2d4vyQpMjJSkydPttuudOnS+uWXX1SwYEHHBwIAAABkIdcCxiuvvKKGDRtq9erVCg0N1alTp8zb0ZYoUUINGzZU//791aFDhwxt58yZo1atWmnGjBmKiIhQUlKSSpQooWbNmmnEiBEKDAzUuHHjshzDp59+qnbt2mnq1Knatm2b4uPjVaZMGXXo0EH//e9/7+izHPz8/LR8+XKtW7dO3377rbZs2aLo6GglJCTI29tblSpVUmBgoDp27Ki2bduma/vJJ5+oS5cuWr16tbZv367o6GjFxMTIxcVFAQEBeuSRRzRkyBA1btw4Xbty5copIiJCq1ev1rp163TkyBGdOXNGV65ckY+Pj2rWrKnOnTtr6NChKlSo0B17LwAAAHDvshm5/SAI3JeioqIUEBAgSSo9PFiu3kWdPCIAAHCnRU7q6OwhwIG0n9dOnDhh2R/fnXYXKQAAAAD3HgIGAAAAAMsQMAAAAABYhoABAAAAwDIEDAAAAACWIWAAAAAAsAwBAwAAAIBlcvVJ3oAkhY1pfUcfaggAAADnYQUDAAAAgGUIGAAAAAAsQ8AAAAAAYBkCBgAAAADLEDAAAAAAWIaAAQAAAMAyBAwAAAAAliFgAAAAALAMAQMAAACAZQgYAAAAACxDwAAAAABgGQIGAAAAAMsQMAAAAABYhoABAAAAwDIEDAAAAACWIWAAAAAAsAwBAwAAAIBlCBgAAAAALEPAAAAAAGAZAgYAAAAAyxAwAAAAAFiGgAEAAADAMgQMAAAAAJYhYAAAAACwDAEDAAAAgGUIGAAAAAAsQ8AAAAAAYBkCBgAAAADLEDAAAAAAWIaAAQAAAMAyBAwAAAAAliFgAAAAALAMAQMAAACAZQgYAAAAACxDwAAAAABgGQIGAAAAAMsQMAAAAABYhoABAAAAwDKuzh4A7n2N3lsrV++izh4GgGyKnNTR2UMAANzFWMEAAAAAYBkCBgAAAADLEDAAAAAAWIaAAQAAAMAyBAwAAAAAliFgAAAAALAMAQMAAACAZQgYAAAAACxDwAAAAABgGQIGAAAAAMsQMAAAAABYhoABAAAAwDIEDAAAAACWuecDxt69e/Xuu++qXbt2KlOmjDw8POTl5aUqVapowIABCgsLs9t23LhxstlsstlskqSEhAR9+OGHeuihh1SoUCEVKlRIgYGB+vzzz5WcnJzlWDZv3qzu3burePHi8vT0VMWKFfXss8/q0KFDkqQWLVrIZrOpRYsWDvuJiorS6NGj9dBDD8nX11eenp4qW7asnnzySa1fv95uu8jISPN4goODJUlLlixRhw4dVKpUKbm6uma5bwAAAMARV2cPIDdt2LBBLVu2zPB6UlKSDh06pEOHDunrr7/Wa6+9pokTJzrs68yZM2rXrp12796d7vVt27Zp27ZtWrVqlX7++Wfly5d5ZpswYYLefPNNGYZhvnb06FF99dVXmj9/vn744YdsHdPs2bM1cuRIXb16Nd3rJ06c0IkTJ7Ro0SI9/fTT+vLLL+Xqan96DcNQ//799c0332RrvwAAAEB23NMBIzk5WQULFlTHjh3VqlUrVatWTd7e3jp79qz27dunqVOn6tixY5o0aZKqVq2qQYMG2e2re/fuOnDggF544QV17txZfn5++uuvvzR+/HgdOHBAy5Yt08yZMzVs2LAMbRcsWKA33nhDkuTr66tXX31VzZo1k3RjVWPSpEnq1auX/P39HR7PnDlzNGTIEElSrVq1NGzYMNWtW1cFChTQ0aNHNXv2bIWEhGj27NkqXLiwPv74Y7t9ffLJJ/rjjz/UtGlTDR8+XFWrVtXFixcVGRmZ1dsKAAAA2GUz0v5J/R4TGxsrV1dX+fj4ZFqelJSkTp06afXq1SpXrpwOHz4sFxcXs3zcuHF6++23JUlubm5atWpVhlOIzp8/rxo1aujMmTN68MEHM6xwJCYmqmzZsjp79qz8/PwUGhqqqlWrpqvz999/6+GHH9b58+clSc2bN9eGDRvS1Tlx4oSqVaum+Ph4DRgwQLNmzcp0heL111/Xe++9p3z58unAgQPp9hUZGakKFSqY3/fv31/BwcHmKWA5ERUV5bA8OjpagYGBkqTSw4Pl6l00x/sA4ByRkzo6ewgAgDsgKipKAQEBkm581ixTpowl/d7T12AULVrUbriQJHd3d3344YeSpGPHjikiIsJu3ZEjR2Z6fYKfn5+58vHHH3/o0qVL6cp/+uknnT17VpI0duzYDOFCkqpWraqxY8c6PJZPP/1U8fHxKlWqlMPTn95++22VLl1aKSkp+vrrr+325+Pjo88///yWwoUkBQQEOPxKDRcAAAC4v9zTAeNmiYmJOn78uPbv36+9e/dq79696a6JuHn1Ia0+ffrYLatXr565ffTo0XRla9eulSTly5dP/fr1s9tH3759HX7YX7p0qSSpc+fO8vT0tFvP1dVVDz/8sCQpNDTUbr3OnTurUKFCdssBAACAW3FPX4MhSXFxcZo6daq+//577du3T9evX7dbNzY21m5ZtWrV7Jb5+fmZ2//880+6sr1790qSKlSoIF9fX4d9VKxYUYcPH85QdunSJfNOU1999ZW++uoru/2kdfr0abtlDz74YLb6sOfEiRMOy9OeIgUAAID7xz0dMCIjI9WqVasMqwr23HxnprQKFChgtyztnaNuDjAXLlyQJBUrVizL/fv7+2caMFJPscqp+Ph4u2WOwk52WHWOHgAAAO4t93TA6Nevn44ePSqbzaZBgwapV69eql69uvz9/eXh4SFJSklJMS/szqvXu6cNLaNGjdLTTz+drXbu7u52y9JezA4AAABY5Z4NGH/++ae2bNkiSRo9erQmTJiQab3UFYbckrpSkJ1ViJiYmExfL1KkiLkdHx+vWrVqWTM4AAAAwGL37EXe+/btM7d79eplt9727dtzdRw1a9aUdOPi79Tb0Gbm/PnzOnLkSKZl/v7+Kl26tCRpzZo1eXalBQAAALhnA0ZycrK57ehahC+//DJXx9G6dWtJN07F+vbbb+3W+/bbbx0Ghy5dukiSjhw5ku2nfgMAAAB32j0bMKpUqWJuz5s3L9M6X3zxhX7++edcHUdQUJB5gffbb7+tgwcPZqhz8OBB84F+9vz3v/81rxt59tlns1x5CQkJ0R9//HGLowYAAABuzT0bMOrWrWteq/DFF1/oqaee0m+//aadO3dq6dKleuKJJ/Tcc8+pcePGuToOT09PffLJJ5JunAbVsGFDffDBBwoLC1NYWJg++OADNWrUSCkpKWYoyux5GBUqVDBXW86fP6/GjRtryJAh+vnnn7Vz506Fh4dryZIleu2111S5cmV17NhRx48fz9VjAwAAAG52z17kbbPZ9M0336hVq1a6cOGCFixYoAULFqSrU7t2bS1evFilSpXK1bH07t1bR44c0ZtvvqkLFy7o1VdfTVdeoEABLV68WJMmTdLBgwftPkhv4MCByp8/v4YOHarLly9r9uzZmj17dqZ18+XLp4IFC1p+LAAAAIAj9+wKhiTVqVNHERERevbZZ1WuXDm5ubnJz89PgYGB+uijjxQeHq6SJUvekbG8/vrr2rhxo7p166ZixYrJw8ND5cqV0+DBg7V9+3Z16NBBly9fliQVLlzYbj9PPvmkIiMjNWnSJLVo0ULFihWTm5ubChQooIoVK6pz586aPHmyIiMj1bJlyztybAAAAEAqm8EtifKEa9euqXDhwrp69areeOMNjR8/3tlDui1RUVEKCAiQJJUeHixX76JOHhGA7Iqc1NHZQwAA3AFpP6+dOHHCsgcp39MrGHeTn3/+2XySeKNGjZw8GgAAAODWEDDukEOHDtkti4yM1IsvvihJKl68uNq1a3enhgUAAABY6p69yDuvqVatmjp06KBOnTqpZs2aKliwoM6ePav169fryy+/1MWLFyVJH330kVxdmRYAAADcnfgke4dcv35dy5Yt07JlyzItz5cvn95991317dv3Do8MAAAAsA4B4w5ZtmyZli9frq1bt+rMmTM6d+6cPDw8VLp0abVo0ULPP/+8+dwOAAAA4G5FwLhDOnXqpE6dOjl7GAAAAECu4iJvAAAAAJZhBQO5LmxMa8vuqwwAAIC8jRUMAAAAAJYhYAAAAACwDAEDAAAAgGUIGAAAAAAsQ8AAAAAAYBkCBgAAAADLEDAAAAAAWIaAAQAAAMAyBAwAAAAAliFgAAAAALAMAQMAAACAZQgYAAAAACxDwAAAAABgGQIGAAAAAMsQMAAAAABYhoABAAAAwDIEDAAAAACWIWAAAAAAsAwBAwAAAIBlCBgAAAAALEPAAAAAAGAZAgYAAAAAyxAwAAAAAFiGgAEAAADAMgQMAAAAAJYhYAAAAACwDAEDAAAAgGUIGAAAAAAsQ8AAAAAAYBkCBgAAAADLEDAAAAAAWIaAAQAAAMAyBAwAAAAAliFgAAAAALAMAQMAAACAZQgYAAAAACxDwAAAAABgGQIGAAAAAMu4OnsAuPc1em+tXL2LOnsYwD0jclJHZw8BAAC7WMEAAAAAYBkCBgAAAADLEDAAAAAAWIaAAQAAAMAyBAwAAAAAliFgAAAAALAMAQMAAACAZQgYFjl+/LiGDRumSpUqydPTUzabTTabTT///LOzhwYAAADcMTxozwLHjx9XvXr1FBsb6+yhAAAAAE5FwLDAu+++q9jYWLm6umrChAlq1qyZvLy8JEnlypVz8ugAAACAO4eAYYE1a9ZIkrp166ZXXnnFyaMBAAAAnIdrMCxw8uRJSVLVqlWdPBIAAADAuQgYFkhKSpIkubm5OXkkAAAAgHMRMG5RcHCweaeoVG+//bb5ms1m08CBA9O1MQxDP/zwg3r06KGAgAB5enrK19dXgYGBGj9+vC5evOhwn2FhYXrjjTfUokULlShRQu7u7vL29laNGjU0fPhw7d+/32H7gQMHymazqXz58pKk6Ohovfrqq6pZs6YKFSokm82mDRs23MK7AQAAANzANRh3SExMjIKCgvT777+nez0xMVHbtm3Ttm3bNG3aNC1dulQNGzbM0D44OFiDBg3K8Pq1a9d04MABHThwQDNnztTUqVP13HPPZTmesLAwde7cmTtfAQAAwFIEjFvUrVs31a9fX5JUu3ZtSdLw4cPTfbj39fWVJMXFxal58+Y6cOCA3N3dNWjQIHXo0EEBAQGKi4vTpk2bNHnyZJ05c0bt27fXrl27Mtx9Kjk5Wb6+vurSpYuaN2+uKlWqqGDBgjp16pR27typqVOnKjY2ViNGjFC1atXUqlUru2O/cuWKevTooYSEBL3++ut69NFHVaBAAe3Zs0clS5a0+q0CAADAfYSAcYt8fHzk4+OT7rVixYqpVq1aGeq+9tprOnDggAoXLqw1a9aYwSRVkyZN1KdPHz388MOKjo7WG2+8oW+++SZdnfbt2+upp55SgQIF0r1et25ddezYUS+88IKaNWumP/74Q2PHjnUYMM6dOycvLy9t2bJF//rXv8zXGzRokN3DV1RUlMPy6OjobPcFAACAewfXYOSy2NhYzZo1S5L0zjvvZAgXqcqVK6c333xTkrRw4ULFx8enKy9dunSGcJFW4cKF9c4770iStmzZonPnzjkc1yuvvJIuXORUQECAw6/AwMBb7hsAAAB3L1YwctnKlSuVkJAgSerZs6fDus2aNZN047qKHTt2qGnTpnbrxsXFKSYmRnFxcTIMQ1L6u1jt3r3b4SpGnz59sn0MAAAAQHYRMHLZ9u3bze2cXN9w+vTpDK/FxsZq8uTJ+vHHH3Xw4EEzWGTG0cXbXl5eqlixYrbHkpkTJ044LI+OjmYVAwAA4D5EwMhlZ8+evaV2N58itWPHDrVr1y7LU59SXb161W7ZzdeO3IoyZcrcdh8AAAC49xAwctn169clSe7u7tqxY0e226X9AJ+UlKSePXvq3LlzcnNz08iRI9W1a1dVrVpVvr6+8vDwkCQdOXJElSpVkiSHqxsuLi63cigAAABAlggYuaxIkSKSboSEIkWK3NJtYNetW6cjR45IkqZNm6Znnnkm03oXLly49YECAAAAFuAuUrmsbt265vaqVatuqY99+/aZ27169bJbL+31HgAAAIAzEDByWfv27c27O02ZMkXJyck57iNtm5uvzUiVkpKiGTNm3NogAQAAAIsQMHJZ6dKlNWjQIEk3bh07bNgwhyHj7Nmz5nMzUlWpUsXcnjdvXqbtRo8erZ07d1owYgAAAODWcQ3GHfDxxx9r69at2rt3r+bMmaOwsDANHTpU9erVk5eXly5evKh9+/ZpzZo1CgkJUe3atTVkyBCzfbt27VSsWDGdPXtWr7/+uo4dO6YuXbqoaNGiOnTokGbOnKm1a9eqcePG+v333514pAAAALjfETDuAC8vL23cuFF9+vTRihUrtH//fo0aNcpufW9v73TfFyxYUF9//bW6deumhIQETZ8+XdOnT09Xp0WLFvr8889Vq1at3DgEAAAAIFs4ReoO8fPz0/Lly7V27VoNGjRIVapUkZeXl1xdXeXn56cGDRro+eefV0hIiFavXp2hfbt27bR9+3b17dtXpUqVkpubm/z9/dW8eXPNmDFDa9euVcGCBZ1wZAAAAMD/sRmOHpgA3KKoqCgFBARIkkoPD5ard1Enjwi4d0RO6ujsIQAA7gFpP6+dOHHCsgcps4IBAAAAwDIEDAAAAACWIWAAAAAAsAwBAwAAAIBlCBgAAAAALEPAAAAAAGAZAgYAAAAAy/Akb+S6sDGtLbuvMgAAAPI2VjAAAAAAWIaAAQAAAMAyBAwAAAAAliFgAAAAALAMAQMAAACAZQgYAAAAACxDwAAAAABgGQIGAAAAAMsQMAAAAABYhoABAAAAwDIEDAAAAACWIWAAAAAAsAwBAwAAAIBlCBgAAAAALEPAAAAAAGAZAgYAAAAAyxAwAAAAAFiGgAEAAADAMgQMAAAAAJYhYAAAAACwDAEDAAAAgGUIGAAAAAAsQ8AAAAAAYBkCBgAAAADLEDAAAAAAWIaAAQAAAMAyBAwAAAAAliFgAAAAALAMAQMAAACAZQgYAAAAACxDwAAAAABgGQIGAAAAAMsQMAAAAABYhoABAAAAwDIEDAAAAACWIWAAAAAAsAwBAwAAAIBlCBgAAAAALOPq7AHg3tfovbVy9S7q7GHgLhQ5qaOzhwAAAHKIFQwAAAAAliFgAAAAALAMAQMAAACAZQgYAAAAACxDwAAAAABgGQIGAAAAAMsQMAAAAABYhoBxC4KDg2Wz2WSz2RQZGens4QAAAAB5BgEDAAAAgGUIGAAAAAAsQ8AAAAAAYBkCBgAAAADLEDAAAAAAWIaAkUtiYmL0xhtvqG7duvLx8ZGnp6fKly+vfv36acuWLZm2mTdvnnl3qjVr1mS5j+eee042m03u7u46d+5cpnX++usvvfDCC6pZs6YKFy6s/Pnzq2LFiho0aJB27tx5W8cIAAAA3IyAkQtWrVqlypUra8KECYqIiNClS5eUmJioY8eO6dtvv1XTpk01YsQIpaSkpGvXvXt35c+fX5L03XffOdxHcnKyFi9eLEl67LHHVKRIkQx1xo8fr1q1aumzzz7T/v37dfnyZSUkJOjo0aMKDg5W/fr1NXbsWIuOGgAAACBgWC4iIkKdO3fW5cuX5ebmplGjRmn9+vUKDw/XV199pQoVKkiSpk2bptGjR6drW6hQIXXp0kWStGTJEiUkJNjdz8qVKxUbGytJ6tOnT4byt956S2+99ZaSk5P1yCOPaNasWQoNDdX27dv13Xff6eGHH5ZhGHrnnXf02WefWXX4AAAAuM/ZDMMwnD2Iu01wcLAGDRokSTp69KjKly9vlgUGBmrbtm1ycXFRSEiI2rZtm67thQsX1KRJE+3fv1/58uXTH3/8oZo1a5rly5YtM0PG4sWL9fjjj2c6hj59+mj+/Pny8vLS2bNnzZUPSdq2bZsaNWqklJQUvfHGGxo/fnyG9ikpKRowYIC+/fZbFSpUSMePH5ePj0+234OoqCiH5dHR0QoMDJQklR4eLFfvotnuG0gVOamjs4cAAMA9KyoqSgEBAZKkEydOqEyZMpb0ywqGhcLDw7Vt2zZJ0pAhQzKEC0ny9fXVjBkzJN34kD99+vR05WlPd7J3mlRcXJyWLl0qKf1pVanef/99paSkqF69enrnnXcy7SNfvnz67LPP5OHhoX/++Uc//PBDDo5UCggIcPiVGi4AAABwfyFgWCjthdlPP/203XqNGzdW9erVM7SRJDc3N/Xs2VOSFBISoosXL2Zov3TpUsXFxUnKeHrUtWvXtHz5cknS448/LpvNZnccPj4+ql27tiQpNDTUbj0AAAAguwgYFtq7d68kyd3dXXXr1nVYt2HDhpKkgwcPKikpKV1ZamhISkrKdGUhdWWjePHiat26dbqy/fv3Kz4+XpI0evRo865U9r62b98uSTp9+nSOjvXEiRMOv8LDw3PUHwAAAO4Nrs4ewL3k/PnzkiQ/Pz+5ujp+a0uUKCFJMgxDFy5cUPHixc2yRx55ROXLl1dkZKS+++47DRkyxCyLjY3VqlWrJEm9evWSi4tLun7Pnj17S2NPDSXZZdU5egAAALi3EDBygaPTklI5urbeZrPpqaee0nvvvadNmzbp5MmTKl26tCRp0aJFSk5OlpT53aOuX79ubn/44Yd67LHHsjXmggULZqseAAAA4AgBw0J+fn6SpHPnzik5OdnhKsaZM2ck3QgTvr6+Gcr79u2r9957TykpKVqwYIFefvllSf93elSVKlXUoEGDDO3SPg/j2rVrqlWr1q0fEAAAAJBDXINhodQP80lJSdq1a5fDuqnXKFSpUkXu7u4ZyqtXr25exzF//nxJUmRkpHkxdmarF5JUs2ZNs7/UU6kAAACAO4WAYaE2bdqY27Nnz7ZbLzQ0VPv378/Q5mapIWLXrl06cOCA5s+fb55a9dRTT2XapkCBAuaF3xs2bOBiawAAANxRBAwLBQYGmqctzZo1S6tXr85Q59KlSxo2bJikG8+iGD58uN3+evfurXz5bkzRd999Z65kBAYGqkqVKnbbvf766+Z1IL169dLhw4ft1r1+/brmz5+f5YPzAAAAgOwgYFhsxowZcnd31/Xr19WxY0e99NJL2rBhg7Zv366ZM2fqoYce0p49eyRJL7/8ssNrJEqVKqWWLVtKkqZNm6Z9+/ZJsn96VKrGjRvrrbfeknTjSeN16tTRqFGjFBISol27diksLEzff/+9/v3vf6ts2bLq06dPps/bAAAAAHKKi7wtVqdOHS1btkxPPPGELl++rMmTJ2vy5MkZ6j3//POaOHFilv316dNHa9euNQOAi4uLnnzyySzbjRs3Tj4+Pnrttdd05coVffrpp/r0008zrevu7i5PT88s+wQAAACywgpGLmjbtq0OHTqkMWPGqE6dOvL29paHh4e5WrB582Z9/vnn5ulPjvTo0SPdh//WrVune2aGI6NGjdLhw4f15ptvqlGjRipatKhcXV1VsGBBVa1aVT169NCXX36pkydPqnLlyrd8vAAAAEAqm+HogQzALYqKilJAQIAkqfTwYLl6F3XyiHA3ipzU0dlDAADgnpX289qJEycse5AyKxgAAAAALEPAAAAAAGAZAgYAAAAAyxAwAAAAAFiGgAEAAADAMgQMAAAAAJYhYAAAAACwDE/yRq4LG9PasvsqAwAAIG9jBQMAAACAZQgYAAAAACxDwAAAAABgGQIGAAAAAMsQMAAAAABYhoABAAAAwDIEDAAAAACWIWAAAAAAsAwBAwAAAIBlCBgAAAAALEPAAAAAAGAZAgYAAAAAyxAwAAAAAFiGgAEAAADAMgQMAAAAAJYhYAAAAACwDAEDAAAAgGUIGAAAAAAsQ8AAAAAAYBkCBgAAAADLEDAAAAAAWIaAAQAAAMAyBAwAAAAAliFgAAAAALAMAQMAAACAZQgYAAAAACxDwAAAAABgGQIGAAAAAMsQMAAAAABYhoABAAAAwDIEDAAAAACWIWAAAAAAsAwBAwAAAIBlCBgAAAAALEPAAAAAAGAZAgYAAAAAyxAwAAAAAFiGgAEAAADAMq7OHgDufY3eWytX76LOHsYdFzmpo7OHAAAAcMexggEAAADAMgQMAAAAAJYhYAAAAACwDAEDAAAAgGUIGAAAAAAsQ8AAAAAAYBkCBgAAAADLEDCcaODAgbLZbCpfvryzhwIAAABYgoABAAAAwDIEDAAAAACWIWA4UXBwsAzDUGRkpLOHAgAAAFiCgAEAAADAMgQMAAAAAJbJ9YBx6tQpvfbaa3rooYdUuHBhubu7q0SJEqpdu7Z69+6t4OBgXb582awfGRkpm80mm82m4OBgh32XL19eNptNAwcOzFAWHBxs9hMZGanExER98sknatSokYoWLSqbzaZx48bZ7Wvbtm3q3bu3AgIC5OnpqYCAAA0cOFAHDhywO54NGzaY+9ywYYNSUlI0Z84ctWzZUsWLF1e+fPnSjTU7d5H66aef1K1bN5UpU0YeHh4qVKiQKlasqKZNm+rNN99UeHi4w/coPDxczzzzjKpWrSovLy8VLFhQ1apV0/PPP6+DBw86bAsAAADklGtudr5582Z16tQpXYCQpDNnzujMmTPau3evvv/+exUtWlSdOnXKtXHExsYqKChIERER2ao/Z84cDRs2TMnJyeZrUVFRmjdvnr7//nvNmzdPTz75pMM+EhIS1K5dO61Zs+aWxnz9+nX17t1bixcvTvd6UlKSrly5oqNHj2rLli1avny5tm/fnqF9cnKyXnjhBX3xxRcZyv766y/99ddfmjlzpqZNm6ZnnnnmlsYIAAAA3CzXAkZiYqJ69eqly5cvq1ChQho+fLhatmypYsWK6dq1azp27JhCQ0P1448/5tYQTE8//bT27Nmj/v3768knn1SJEiV0/PhxeXh4ZKgbERGh+fPnq1ixYho9erQCAwOVkJCgkJAQffLJJ0pMTFTfvn1VoUIFBQYG2t3nq6++qj/++ENdunTRwIEDVa5cOZ05cyZD2LLniy++MMNFkyZNNGTIEFWqVEleXl46f/689u7dq+XLl+v8+fN2j/nrr7+WJLVv3159+vRR1apVZbPZFBERoU8++UT79u3T0KFDVaJECXXu3Dlb4wIAAAAcybWA8fvvv+vUqVOSpPnz52dYoWjYsKF69uypDz/8UPHx8bk1DEnSH3/8odmzZ2vw4MHmaw899FCmdXfv3q1y5copLCxMJUqUMF9v1qyZ2rVrp7Zt2yo5OVnPP/+8tm3b5nCfb775pt55551bGvOiRYsk3Xif1q9fL1fX9FPVqlUrvfDCC5kGjB9//NEMFzNnztSQIUPSldevX199+/ZVx44dtW7dOr3wwgtq3759hn04EhUV5bA8Ojo6230BAADg3pFr12CcPn3a3G7WrJndeq6urvL29s6tYUi68WE8bbjIyscff5wuXKRq2bKleTrR9u3bHQaMqlWrauzYsTkf7P+X+v498sgjDj/4+/n5ZXht4sSJkqSgoKAM4SKVp6enPv/8c0k3rnvZsGFDjsYXEBDg8MvR6g4AAADuXbkWMEqWLGluz507N7d2ky19+vTJdl1fX1917drVbnnaoOLo+oonn3xSLi4u2d7vzVLfv2XLlik2Njbb7U6ePKkdO3ZIknr27OmwbvXq1VW0aFFJUmho6C2OFAAAAPg/uRYwmjRpoooVK0qSRo0apcDAQE2cOFFbt25VUlJSbu02Uw8++GC269atW9fhikGdOnXk7u4uSdq7d68l+8zMgAEDJEmHDh1S5cqVNXjwYC1YsCDLU5PSXvDdu3dv865W9r5Sw0vaFafsOHHihMOvrO5uBQAAgHtTrl2D4ebmpmXLlunxxx/XgQMHtG3bNvOUovz586t58+bq16/fbf+lPzt8fX2zXbdYsWIOy11dXeXn56fTp0/bvcA6p/vMzODBg3X48GF98MEHunTpkubOnWuuBFWqVEndunXTc889Z4a4VGfPnr2l/eX0OpgyZcrc0n4AAABwb8vV29TWqFFDe/bs0bJly7Rs2TJt3LhRhw8f1tWrV7VixQqtWLFCkydPVkhISJYf7G9HTgKMzWbLso5hGJbu054JEyZo6NCh+u6777R27VqFhYUpPj5ehw8f1scff6ypU6dq6tSpevbZZ802169fN7e/++67bK+k3G4gAgAAAKRcDhjSjQ/a3bp1U7du3STduLvQ8uXLNX36dO3YsUM7duzQsGHD9NNPP0mS8uX7v7O2UlJSHPYdFxdn+XjPnDnjsDw5OVkXLlyQlPkF1lYrV66cxowZozFjxujatWsKDw/X4sWL9dVXXykhIUHPPfecGjZsqLp160qSihQpYra12WyqVatWro8RAAAASJXrT/K+WcmSJTV48GCFhoaat4r99ddfdfXqVUlSoUKFzLqpH+Qzc+7cuRxd/JxdERER6R6wd7Pdu3eb15Dc6Q/vbm5uaty4sT755BPNnz9f0o3VlB9++MGskxo0JGnVqlV3dHwAAADAHQ8Yqdzc3NS8eXNJN1YFLl68KOnGqTo+Pj6SlOkTqlMtWLAgV8Z1/vx5LVu2zG75nDlzzO02bdrkyhiyo3Xr1uZ22qBVuXJl1ahRQ5L0/fff6/jx43d8bAAAALh/5VrA2Lx5sw4dOmS3PCkpSRs3bpQkeXl5yd/f3yxLfW7G0qVLdfjw4QxtDxw4oLfeesviEf+fF198MdNTpTZu3KgZM2ZIkurVq6cGDRrk2hi+/fZbhyspaVcnKlSokK7sjTfekCQlJCSoe/fuiomJsdtPYmKipk+froSEhNscMQAAAJCL12CsXbtW48ePV9OmTdWxY0c9+OCD8vf319WrV/X333/ryy+/1M6dOyVJQ4YMSXdr2Oeee06//PKLrl69qhYtWmjcuHGqW7eurly5ojVr1ujTTz9VsWLF5Orq6vDD863417/+pf3796tevXoaPXq0AgMDlZiYqJCQEE2ZMkXJyclydXXVtGnTLN3vzfr166eXX35Z3bt31yOPPKJKlSrJ09NTZ86c0erVq/XFF19IuhHO+vbtm65t7969tXLlSs2bN087duxQjRo1NGzYMDVv3lz+/v6Ki4vT4cOHtXnzZi1ZskTnz59X//79c/V4AAAAcH/I1Yu8U1JStHHjRnOlIjPdu3c3nzydql27dnrhhRc0depURUVFZXgadUBAgJYuXaoOHTpYPuY6depoxIgRGj58uEaMGJGh3N3dXfPmzVPDhg0t3/fNzpw5oy+++MIMEzfz8fHRwoULM71l7OzZs1W8eHF9/PHHio2N1YQJEzRhwoRM+ylYsGCu3yoYAAAA94dcCxivvPKKGjZsqNWrVys0NFSnTp0yn9FQokQJNWzYUP3797cbEj799FM1atRIX375pSIiInTt2jWVLVtWQUFBevnll9PdLclqQ4YMUa1atTRlyhRt2bJFsbGx8vf3V+vWrfXqq6+a1zjkpj///FOrV6/W2rVr9ffff+vMmTO6dOmSChUqpAceeECPPfaYhg8fbvf2vi4uLnr//ff19NNPa8aMGVq3bp0iIyN1+fJlFShQQGXLllWdOnXUtm1bBQUFKX/+/Ll+TAAAALj32YzsPNThPlC+fHkdO3ZMAwYMUHBwsLOHc9eLiopSQECAJKn08GC5ehd18ojuvMhJHZ09BAAAALvSfl47ceKEZQ9SdtpdpAAAAADcewgYAAAAACxDwAAAAABgGQIGAAAAAMsQMAAAAABYJlefg3E3iYyMdPYQAAAAgLseKxgAAAAALMMKBnJd2JjWlt1XGQAAAHkbKxgAAAAALEPAAAAAAGAZAgYAAAAAyxAwAAAAAFiGgAEAAADAMgQMAAAAAJYhYAAAAACwDAEDAAAAgGUIGAAAAAAsQ8AAAAAAYBkCBgAAAADLEDAAAAAAWIaAAQAAAMAyBAwAAAAAliFgAAAAALAMAQMAAACAZQgYAAAAACzj6uwB4N6UnJxsbkdHRztxJAAAAMhM2s9oaT+73S4CBnJFTEyMuR0YGOjEkQAAACArMTExKl++vCV9cYoUcsWZM2ecPQQAAAA4ASsYyBXVqlUzt7du3aqAgAAnjgbZFR0dba44hYeHq2TJkk4eEbKLubs7MW93J+bt7sS8ZZScnGyedVK7dm3L+iVgIFd4enqa2wEBASpTpowTR4NbUbJkSebtLsXc3Z2Yt7sT83Z3Yt7+j1WnRaXFKVIAAAAALEPAAAAAAGAZAgYAAAAAyxAwAAAAAFiGgAEAAADAMgQMAAAAAJYhYAAAAACwjM0wDMPZgwAAAABwb2AFAwAAAIBlCBgAAAAALEPAAAAAAGAZAgYAAAAAyxAwAAAAAFiGgAEAAADAMgQMAAAAAJYhYAAAAACwDAEDAAAAgGUIGLDc8ePH9fLLL6t69eoqWLCg/Pz8FBgYqI8++kjx8fHOHt5d4ezZs/r111/11ltvqX379ipatKhsNptsNpsGDhyY4/5WrFih7t27q0yZMvLw8FCZMmXUvXt3rVixItt9xMfH68MPP1RgYKD8/Pzk5eWl6tWr6+WXX9bx48ez3c++ffv07LPPqnLlysqfP7/8/f3VrFkzffXVV0pOTs7xseUlO3fu1Hvvvaf27dsrICBAHh4e8vLyUtWqVTVw4EBt3rw5R/0xb3fG5cuX9f333+ull15S8+bNVblyZRUuXFju7u4qVqyYWrRooQ8++EDnzp3LVn/Mm/O98sor5u9Mm82mDRs2ZNmGebtz0s6No68WLVpk2RfzlkcZgIV+/fVXo3DhwoakTL8eeOAB4/Dhw84eZp5n7/2TZAwYMCDb/aSkpBhDhw512N/QoUONlJQUh/0cOnTIeOCBB+z2UbhwYeO3337LcjyzZs0yPDw87PbTqFEjIzY2NtvHl5c0a9bM4fuc+tWvXz8jMTHRYV/M2521evXqbM1d0aJFjRUrVtjth3nLGyIiIgxXV9d0x7p+/Xq79Zm3Oy87/94kGc2bN7fbB/OWtxEwYJmIiAijQIEChiTDy8vLmDBhgrF161Zj7dq1xjPPPGP+I6tWrZrxzz//OHu4eVraX0oBAQFG27Ztze9zEjDGjBljtqtbt66xYMECIzw83FiwYIFRt25ds+z111+328c///xjVKtWzaz7zDPPGGvXrjW2bt1qTJgwwfDy8jIkGQUKFDB2795tt58VK1YY+fLlMyQZxYsXN6ZOnWr873//M5YvX250797d7L9Zs2bG9evXc/J25QmVKlUyJBmlSpUy/v3vfxs//PCDER4eboSGhhqTJ082SpcubR5j7969HfbFvN1Zq1evNgICAoz+/fsbn376qbFkyRIjNDTU+P33342FCxcaTzzxhOHi4mJIMtzd3e2+X8yb812/ft1o0KCBIckoVqxYtgIG83bnpR7H8OHDjT179tj9OnLkiN0+mLe8jYABy7Ro0cKQZLi6uhpbt27NUP7BBx+Y/8jefvttJ4zw7vHWW28Zy5YtM06fPm0YhmEcPXo0xwHj4MGD5l/x6tevb8THx6crj4uLM+rXr2/O2aFDhzLtZ+zYsea+P/jggwzlW7duNffTsmXLTPu4du2aUblyZUOS4e3tnem+nnvuOXM/8+bNy9Yx5iUdO3Y0Fi5caCQnJ2daHhMTY1StWtU8xk2bNmVaj3m78+zNWVo//fSTeZzdu3fPUM685Q1Tpkwx/5A1evToLAMG8+YcqccwduzYW2rPvOV9BAxYIjw83PzHM2zYsEzrXL9+3ahevbohyfD19TWSkpLu8CjvXrcSMNL+QgsNDc20TmhoqFlnxIgRGcqTkpIMHx8fQ5JRvXp1u395GTZsmNnP9u3bM5QvWrTILJ84cWKmfcTFxRm+vr6GJKNWrVrZOsa7zbJly8z34YUXXsi0DvOWd6X+pbNo0aIZypg35zt+/Lj5F+f169en+/BoL2Awb85xuwGDecv7CBiwRNqlyrCwMLv1Jk6caNZbtWrVHRzh3S2nASMlJcU8JadatWoO66aee1qmTJkM56quWrXK3O+kSZPs9pH2F/mYMWMylD/11FNmeXR0tN1+0v4i//vvv7M4yrvPP//8Yx5fx44dM5Qzb3lbvXr1DOnGKaBpMW95Q6dOndL9jswqYDBvznM7AYN5uztwFylYIvXuOAULFlS9evXs1mvevLm5vWXLllwf1/3q6NGjOnnypKT073lmUsujoqIUGRmZriztXY8c9VO/fn0VLFhQUubzmtrPAw88oBIlSmQ5Fnv93O2SkpLM7Xz5Mv76Zd7yrgMHDigiIkKSVK1atXRlzJvzLVq0SL/++qv8/Pz04YcfZqsN83Z3Yt7uDgQMWOLAgQOSpMqVK8vV1dVuvbT/Mae2gfXSvrc3fxi6maM5yW4/rq6uqlSpUqZ9XLlyRVFRUbc9lnvBxo0bze3M3gvmLW+Jj4/XwYMHNXnyZLVs2VLXr1+XJP373/9OV495c66LFy+ac/L+++/L398/W+2YN+dbvHixHnjgAeXPn1+FChVSlSpVNGDAAK1fv95uG+bt7kDAwG1LSEhQbGysJKlMmTIO6/r6+pp/CThx4kSuj+1+lfa9zWpOAgICMm2X9vuCBQvKx8cnW/3ExMQoMTHRfD0qKkqGYdz2WO52KSkpmjRpkvl9z549M9Rh3pwvODjYvAd/wYIFVbVqVb300ks6c+aMJOnll19Wnz590rVh3pzrlVde0enTp/XII4/o6aefznY75s359u/fr7///lsJCQm6cuWKDh06pK+//lqtWrVSUFCQLl26lKEN83Z3sP+nZiCb/vnnH3Pby8sry/oFCxZUXFycrly5kpvDuq/lZE5SA5+kDHOS2k925zVtPx4eHpaO5W43ZcoUhYeHS5KCgoJUv379DHWYt7yrTp06+vLLL9WwYcMMZcyb82zZskWzZs2Sq6urvvzyS9lstmy3Zd6cp0CBAurSpYtat26tatWqycvLSzExMdq4caO+/PJLnTt3Tj///LO6du2q1atXy83NzWzLvN0dCBi4bQkJCea2u7t7lvVT/2FevXo118Z0v8vJnKTOh5RxTlL7ycm83tyPVWO5m23cuFGvvfaaJKlYsWL64osvMq3HvDlft27dzPB39epVHT58WIsWLdJPP/2kPn366JNPPlGnTp3StWHenCMpKUlDhw6VYRj6z3/+o9q1a+eoPfPmPCdPnsx01eDRRx/VyJEj1b59e+3atUsbN27UF198oRdeeMGsw7zdHThFCrfN09PT3E57Eas9qcuL+fPnz7Ux3e9yMidpl3tvnpPUfnIyrzf3Y9VY7lb79u1TUFCQkpOT5eHhoUWLFql48eKZ1mXenM/Hx0e1atVSrVq11KBBA/Xq1UtLlizR119/rSNHjqhr164KDg5O14Z5c4733ntPBw4cUNmyZTV27Ngct2fenMfRKUnFixfXDz/8YH5g/+yzz9KVM293BwIGbluhQoXM7ews+8XFxUnK3rIkbk1O5iR1PqSMc5LaT07m9eZ+rBrL3ejo0aNq27atLly4IBcXFy1YsMDh3UqYt7yrX79+euKJJ5SSkqIRI0bowoULZhnzduf9+eefmjhxoqQbH0DTnn6SXcxb3lWxYkU9+uijkqRDhw7p1KlTZhnzdncgYOC2eXp6qmjRopJk3k3BngsXLpj/yNJe8ARrpb3YLKs5SXux2c1zktpPXFycLl68mK1+/P390y0FWzWWu82pU6fUpk0bnTp1SjabTXPmzFFQUJDDNsxb3ta1a1dJN97X5cuXm68zb3felClTlJSUpIoVKyo+Pl7ff/99hq+9e/ea9detW2e+nvp/EPOWt9WoUcPcTr0trcS83S0IGLBE9erVJd34S0NycrLden/++WeGNrBe2l/Mad/zzDiak+z2k5ycrMOHD2fah5eXl/nL9HbGcjeJjY3Vo48+qiNHjki68RfW/v37Z9mOecvb0t7+9NixY+Y283bnpZ5ucuTIEfXu3TvTrx9//NGsP378ePP1mJgYScxbXpd6d6abMW93BwIGLNGkSRNJN/4SsGPHDrv10j4HoHHjxrk+rvtVhQoVVKpUKUnp3/PMbNq0SZJUunRplS9fPl1Z6rxm1c/27dvNvwpmNq+p/fz11186ffq03X7uhZ+PS5cuqV27dtq/f78kadKkSXr++eez1ZZ5y9vS/hU17SkOzNvdiXnL21J/h0oy50li3u4aTnyKOO4h//vf/wxJhiRj2LBhmda5fv26Ub16dUOS4ePjYyQlJd3hUd69jh49ar6/AwYMyFab4cOHm21CQ0MzrRMaGmrWee655zKUJyYmGoULFzYkGdWrVzdSUlIy7WfYsGFmP+Hh4RnKFy5caJZPnDgx0z7i4uIMX19fQ5JRo0aNbB1jXhMXF2c0btzYPNbXX389x30wb3lXhw4dzPdj/fr16cqYt7xn7NixducrFfOWNx0+fNhwc3MzJBkVK1bMUM685X0EDFimadOmhiTD1dXV2Lp1a4byDz74wPxHOHbs2Ds/wLvYrQSMv/76y3B1dTUkGfXr1zfi4+PTlcfHxxv169c35+zvv//OtJ8333zT3PcHH3yQoXzr1q3mfpo3b55pH0lJSUalSpUMSYa3t7dx6NChDHWee+45cz9z587N1jHmJYmJiUbbtm3NY/j3v/99S/0wb3fe3LlzjatXrzqsM3nyZPM4y5cvb1y7di1dOfOW92QnYDBvd94vv/yS4d9PWqdPnzbq1q1rHufHH3+coQ7zlvcRMGCZnTt3Gvnz5zckGV5eXsZ7771nhIaGGuvWrTOGDh1q/uOqWrWqcfnyZWcPN0/bvHmzMXfuXPPrww8/NN+/xo0bpytz9MvqtddeM9vVrVvX+P77741t27YZ33//fbpf4KNHj7bbx+XLl42qVauadYcOHWqsW7fOCA0NNd577z3Dy8vLkGTkz5/f2LVrl91+fvvtNyNfvnyGJKN48eLGZ599Zvzvf/8zVqxYYfTo0cPsv0mTJkZycvJtvHvO0b17d/MYWrVqZfzxxx/Gnj177H799ddfdvti3u6scuXKGX5+fsYzzzxjzJs3z9iyZYsRERFhbN682Zg+fXq6VSl3d3dj9erVmfbDvOUt2QkYhsG83WnlypUzSpUqZYwcOdKYP3++sXXrVmPXrl3G6tWrjddff90oUqRIuuNMSEjItB/mLW8jYMBSv/zyi+Ht7W3+Y7r5q2rVqsbBgwedPcw8b8CAAXbfw8y+7Ll+/boxePBgh22ffvpp4/r16w7Hc/DgQaNKlSp2+/D29jaWLVuW5XHNmDHDcHd3t9tPYGCgERMTk+P3Ky/IyXxJMsqVK2e3L+btzipXrly25qxMmTLGqlWr7PbDvOUt2Q0YzNudld1/bz169DAuXLhgtx/mLW8jYMBykZGRxn/+8x+jatWqRoECBQwfHx+jfv36xvvvv2/ExcU5e3h3BasCRqrffvvN6Nq1q1GqVCnD3d3dKFWqlNG1a1cjJCQk22O6cuWK8f777xv169c3fHx8jAIFChgPPPCA8Z///MeIjIzMdj979uwxnnnmGaNixYqGp6enUaRIEaNJkybGF1984XDZPK+zMmCkYt7ujEOHDhlffvml8eSTTxoPPvigUbx4ccPV1dXw8vIyKlWqZPTo0cOYO3dutn9/MW95Q3YDRirm7c7YsGGD8fbbbxuPPfaYUbVqVcPPz89wdXU1fHx8jNq1axvDhg3L9DRre5i3vMlmGHbuAwYAAAAAOcRtagEAAABYhoABAAAAwDIEDAAAAACWIWAAAAAAsAwBAwAAAIBlCBgAAAAALEPAAAAAAGAZAgYAAAAAyxAwAAAAAFiGgAEAAADAMgQMAAAAAJYhYAAAAACwDAEDAAAAgGUIGAAAAAAsQ8AAAAAAYBkCBgAAAADLEDAAAAAAWIaAAQAAAMAyBAwAAAAAliFgAAAAALAMAQMAAACAZQgYAAAAACxDwAAAAABgGQIGAAAAAMsQMAAAAABY5v8B9z7Mz9XKpcAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(4, 3))\n",
    "emotions_df['label_name'].value_counts(ascending=True).plot.barh()\n",
    "plt.title('freq of labels')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c123332a",
   "metadata": {},
   "source": [
    "### text length analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f4334532",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-16T03:19:29.180285Z",
     "start_time": "2023-04-16T03:19:28.839967Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, '')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 800x600 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1280x960 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(4, 3))\n",
    "emotions_df['words per tweet'] = emotions_df['text'].str.split().apply(len)\n",
    "emotions_df.boxplot('words per tweet', by='label_name', \n",
    "#                     showfliers=False, \n",
    "                    grid=False, \n",
    "                    color='black')\n",
    "plt.suptitle('')\n",
    "plt.xlabel('')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e9ad1097",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-16T03:19:41.946028Z",
     "start_time": "2023-04-16T03:19:41.937780Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "66\n",
      "6322\n"
     ]
    }
   ],
   "source": [
    "print(emotions_df['words per tweet'].max())\n",
    "print(emotions_df['words per tweet'].idxmax())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1a61caba",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-16T03:19:50.013668Z",
     "start_time": "2023-04-16T03:19:50.000778Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "text               i guess which meant or so i assume no photos n...\n",
      "label                                                              0\n",
      "label_name                                                   sadness\n",
      "words per tweet                                                   66\n",
      "Name: 6322, dtype: object\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'i guess which meant or so i assume no photos no words or no other way to convey what it really feels unless you feels it yourself or khi bi t au th m i bi t th ng ng i b au i rephrase it to a bit more gloomy context unless you are hurt yourself you will never have sympathy for the hurt ones'"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(emotions_df.iloc[6322])\n",
    "emotions_df.iloc[6322]['text']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "bd0a6880",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-16T03:20:19.372025Z",
     "start_time": "2023-04-16T03:20:19.363824Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "4150\n"
     ]
    }
   ],
   "source": [
    "print(emotions_df['words per tweet'].min())\n",
    "print(emotions_df['words per tweet'].idxmin())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "85843cc5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-16T03:20:25.313810Z",
     "start_time": "2023-04-16T03:20:25.300632Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "text               earth crake\n",
       "label                        4\n",
       "label_name                fear\n",
       "words per tweet              2\n",
       "Name: 4150, dtype: object"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emotions_df.iloc[4150]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7922006e",
   "metadata": {},
   "source": [
    "## text => tokens"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2050b9ab",
   "metadata": {},
   "source": [
    "数据集转换为模型接受的输入类型\n",
    "- Subword Tokenization\n",
    "    - WordPiece\n",
    "        - BERT and DistilBERT\n",
    "- hugging face:\n",
    "    - `~/.cache/huggingface/`\n",
    "- tokenizer\n",
    "    - `tokenizer.vocab_size`\n",
    "    - model config\n",
    "        - `tokenizer.model_max_length`\n",
    "        - `tokenizer.model_input_names`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc384efb",
   "metadata": {},
   "source": [
    "### tokenizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "fefa9f6e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-16T03:21:31.998581Z",
     "start_time": "2023-04-16T03:21:31.894927Z"
    }
   },
   "outputs": [],
   "source": [
    "from transformers import AutoTokenizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "a9fa8c2b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-16T03:21:37.331219Z",
     "start_time": "2023-04-16T03:21:36.885441Z"
    }
   },
   "outputs": [],
   "source": [
    "model_ckpt = 'distilbert-base-uncased'\n",
    "tokenizer = AutoTokenizer.from_pretrained(model_ckpt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "eff26029",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-16T03:22:02.988257Z",
     "start_time": "2023-04-16T03:22:02.968686Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[101, 7592, 2088, 102]\n",
      "[101, 7592, 2088, 102]\n",
      "[101, 7592, 2088, 102]\n"
     ]
    }
   ],
   "source": [
    "# uncased\n",
    "print(tokenizer.encode('hello world'))\n",
    "print(tokenizer.encode('HELLO WORLD'))\n",
    "print(tokenizer.encode('Hello World'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "e2ff594e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-16T03:22:27.555382Z",
     "start_time": "2023-04-16T03:22:27.542159Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[101,\n",
       " 1045,\n",
       " 3984,\n",
       " 2029,\n",
       " 3214,\n",
       " 2030,\n",
       " 2061,\n",
       " 1045,\n",
       " 7868,\n",
       " 2053,\n",
       " 7760,\n",
       " 2053,\n",
       " 2616,\n",
       " 2030,\n",
       " 2053,\n",
       " 2060,\n",
       " 2126,\n",
       " 2000,\n",
       " 16636,\n",
       " 2054,\n",
       " 2009,\n",
       " 2428,\n",
       " 5683,\n",
       " 4983,\n",
       " 2017,\n",
       " 5683,\n",
       " 2009,\n",
       " 4426,\n",
       " 2030,\n",
       " 1047,\n",
       " 4048,\n",
       " 12170,\n",
       " 1056,\n",
       " 8740,\n",
       " 16215,\n",
       " 1049,\n",
       " 1045,\n",
       " 12170,\n",
       " 1056,\n",
       " 16215,\n",
       " 12835,\n",
       " 12835,\n",
       " 1045,\n",
       " 1038,\n",
       " 8740,\n",
       " 1045,\n",
       " 16360,\n",
       " 13492,\n",
       " 3366,\n",
       " 2009,\n",
       " 2000,\n",
       " 1037,\n",
       " 2978,\n",
       " 2062,\n",
       " 24067,\n",
       " 2100,\n",
       " 6123,\n",
       " 4983,\n",
       " 2017,\n",
       " 2024,\n",
       " 3480,\n",
       " 4426,\n",
       " 2017,\n",
       " 2097,\n",
       " 2196,\n",
       " 2031,\n",
       " 11883,\n",
       " 2005,\n",
       " 1996,\n",
       " 3480,\n",
       " 3924,\n",
       " 102]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 101([CLS]) 开始，以 102 ([SEP]) 结束\n",
    "tokenizer.encode(emotions_df.iloc[6322]['text'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "d9e5e3e9",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-16T03:22:48.912662Z",
     "start_time": "2023-04-16T03:22:48.905432Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30522\n",
      "512\n",
      "['input_ids', 'attention_mask']\n"
     ]
    }
   ],
   "source": [
    "print(tokenizer.vocab_size)\n",
    "print(tokenizer.model_max_length)\n",
    "print(tokenizer.model_input_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "4167e955",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-16T03:23:23.631699Z",
     "start_time": "2023-04-16T03:23:23.625571Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100 [UNK]\n",
      "102 [SEP]\n",
      "0 [PAD]\n",
      "101 [CLS]\n",
      "103 [MASK]\n"
     ]
    }
   ],
   "source": [
    "for special_id in tokenizer.all_special_ids:\n",
    "    print(special_id, tokenizer.decode(special_id))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "421f0df4",
   "metadata": {},
   "source": [
    "### tokenize the whole dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "4eb3661f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-16T03:23:52.516614Z",
     "start_time": "2023-04-16T03:23:52.510127Z"
    }
   },
   "outputs": [],
   "source": [
    "def batch_tokenize(batch):\n",
    "    return tokenizer(batch['text'], padding=True, truncation=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "935d79d6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-16T03:24:06.845931Z",
     "start_time": "2023-04-16T03:24:06.601720Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading cached processed dataset at /home/whaow/.cache/huggingface/datasets/emotion/split/1.0.0/cca5efe2dfeb58c1d098e0f9eeb200e9927d889b5a03c67097275dfb5fe463bd/cache-e7e40ac1a20aaacd.arrow\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map:   0%|          | 0/2000 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading cached processed dataset at /home/whaow/.cache/huggingface/datasets/emotion/split/1.0.0/cca5efe2dfeb58c1d098e0f9eeb200e9927d889b5a03c67097275dfb5fe463bd/cache-8b21ce57445713f4.arrow\n"
     ]
    }
   ],
   "source": [
    "# batch_tokenize(emotions['train'])\n",
    "emotions_encoded = emotions.map(batch_tokenize, batched=True, batch_size=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "c94751fd",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-16T03:24:22.415245Z",
     "start_time": "2023-04-16T03:24:22.407207Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatasetDict({\n",
       "    train: Dataset({\n",
       "        features: ['text', 'label', 'input_ids', 'attention_mask'],\n",
       "        num_rows: 16000\n",
       "    })\n",
       "    validation: Dataset({\n",
       "        features: ['text', 'label', 'input_ids', 'attention_mask'],\n",
       "        num_rows: 2000\n",
       "    })\n",
       "    test: Dataset({\n",
       "        features: ['text', 'label', 'input_ids', 'attention_mask'],\n",
       "        num_rows: 2000\n",
       "    })\n",
       "})"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emotions_encoded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "4824d445",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-16T03:26:12.147121Z",
     "start_time": "2023-04-16T03:26:11.743699Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'list'>\n"
     ]
    }
   ],
   "source": [
    "print(type(emotions_encoded['train']['input_ids']))\n",
    "# emotions_encoded['train']['input_ids'][:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "28355b03",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-16T03:26:39.252975Z",
     "start_time": "2023-04-16T03:26:39.248290Z"
    }
   },
   "outputs": [],
   "source": [
    "# list to tensor\n",
    "emotions_encoded.set_format('torch', columns=['label', 'input_ids', 'attention_mask'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "eaf7b8fc",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-16T03:26:51.213741Z",
     "start_time": "2023-04-16T03:26:51.158188Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Tensor"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(emotions_encoded['train']['input_ids'])\n",
    "# emotions_encoded['train']['input_ids'][:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "732c0ab8",
   "metadata": {},
   "source": [
    "# fine-tune transformers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9a5aade",
   "metadata": {},
   "source": [
    "## distilbert-base-uncased"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49aea3b3",
   "metadata": {},
   "source": [
    "- distilbert 是对 bert 的 distill 而来\n",
    "    - 模型结构更为简单，\n",
    "    - bert-base-uncased 参数量：109482240\n",
    "    - distilbert-base-uncased 参数量：66362880"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "39c36c93",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-16T03:27:29.425400Z",
     "start_time": "2023-04-16T03:27:28.193454Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of the model checkpoint at distilbert-base-uncased were not used when initializing DistilBertModel: ['vocab_transform.weight', 'vocab_layer_norm.weight', 'vocab_transform.bias', 'vocab_projector.weight', 'vocab_projector.bias', 'vocab_layer_norm.bias']\n",
      "- This IS expected if you are initializing DistilBertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
      "- This IS NOT expected if you are initializing DistilBertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "DistilBertModel(\n",
       "  (embeddings): Embeddings(\n",
       "    (word_embeddings): Embedding(30522, 768, padding_idx=0)\n",
       "    (position_embeddings): Embedding(512, 768)\n",
       "    (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "    (dropout): Dropout(p=0.1, inplace=False)\n",
       "  )\n",
       "  (transformer): Transformer(\n",
       "    (layer): ModuleList(\n",
       "      (0-5): 6 x TransformerBlock(\n",
       "        (attention): MultiHeadSelfAttention(\n",
       "          (dropout): Dropout(p=0.1, inplace=False)\n",
       "          (q_lin): Linear(in_features=768, out_features=768, bias=True)\n",
       "          (k_lin): Linear(in_features=768, out_features=768, bias=True)\n",
       "          (v_lin): Linear(in_features=768, out_features=768, bias=True)\n",
       "          (out_lin): Linear(in_features=768, out_features=768, bias=True)\n",
       "        )\n",
       "        (sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "        (ffn): FFN(\n",
       "          (dropout): Dropout(p=0.1, inplace=False)\n",
       "          (lin1): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          (lin2): Linear(in_features=3072, out_features=768, bias=True)\n",
       "          (activation): GELUActivation()\n",
       "        )\n",
       "        (output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "      )\n",
       "    )\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from transformers import AutoModel\n",
    "model_ckpt = 'distilbert-base-uncased'\n",
    "model = AutoModel.from_pretrained(model_ckpt)\n",
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "d8072945",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-16T03:28:06.827640Z",
     "start_time": "2023-04-16T03:28:06.478840Z"
    }
   },
   "outputs": [],
   "source": [
    "from transformers_utils import get_params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "596b7c7f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-16T03:28:20.889251Z",
     "start_time": "2023-04-16T03:28:20.878145Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "66362880"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_params(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "65438d54",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-16T03:28:36.824993Z",
     "start_time": "2023-04-16T03:28:35.024464Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.predictions.transform.LayerNorm.bias', 'cls.seq_relationship.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.decoder.weight', 'cls.predictions.bias', 'cls.predictions.transform.dense.bias', 'cls.seq_relationship.weight', 'cls.predictions.transform.LayerNorm.weight']\n",
      "- This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
      "- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "109482240"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from transformers import AutoModel\n",
    "model_ckpt = 'bert-base-uncased'\n",
    "model = AutoModel.from_pretrained(model_ckpt)\n",
    "get_params(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "e88c3348",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-16T03:28:52.500866Z",
     "start_time": "2023-04-16T03:28:52.493212Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.6497511862053003"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "109482240/66362880"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "52d9374a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-16T03:28:59.292347Z",
     "start_time": "2023-04-16T03:28:59.279728Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BertModel(\n",
       "  (embeddings): BertEmbeddings(\n",
       "    (word_embeddings): Embedding(30522, 768, padding_idx=0)\n",
       "    (position_embeddings): Embedding(512, 768)\n",
       "    (token_type_embeddings): Embedding(2, 768)\n",
       "    (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "    (dropout): Dropout(p=0.1, inplace=False)\n",
       "  )\n",
       "  (encoder): BertEncoder(\n",
       "    (layer): ModuleList(\n",
       "      (0-11): 12 x BertLayer(\n",
       "        (attention): BertAttention(\n",
       "          (self): BertSelfAttention(\n",
       "            (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (output): BertSelfOutput(\n",
       "            (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (intermediate): BertIntermediate(\n",
       "          (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          (intermediate_act_fn): GELUActivation()\n",
       "        )\n",
       "        (output): BertOutput(\n",
       "          (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "          (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "          (dropout): Dropout(p=0.1, inplace=False)\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "  )\n",
       "  (pooler): BertPooler(\n",
       "    (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "    (activation): Tanh()\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "13903119",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-16T03:31:05.095455Z",
     "start_time": "2023-04-16T03:31:01.744227Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of the model checkpoint at distilbert-base-uncased were not used when initializing DistilBertForSequenceClassification: ['vocab_transform.weight', 'vocab_layer_norm.weight', 'vocab_transform.bias', 'vocab_projector.weight', 'vocab_projector.bias', 'vocab_layer_norm.bias']\n",
      "- This IS expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
      "- This IS NOT expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
      "Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['pre_classifier.bias', 'pre_classifier.weight', 'classifier.weight', 'classifier.bias']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "DistilBertForSequenceClassification(\n",
       "  (distilbert): DistilBertModel(\n",
       "    (embeddings): Embeddings(\n",
       "      (word_embeddings): Embedding(30522, 768, padding_idx=0)\n",
       "      (position_embeddings): Embedding(512, 768)\n",
       "      (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "      (dropout): Dropout(p=0.1, inplace=False)\n",
       "    )\n",
       "    (transformer): Transformer(\n",
       "      (layer): ModuleList(\n",
       "        (0-5): 6 x TransformerBlock(\n",
       "          (attention): MultiHeadSelfAttention(\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "            (q_lin): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (k_lin): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (v_lin): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (out_lin): Linear(in_features=768, out_features=768, bias=True)\n",
       "          )\n",
       "          (sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "          (ffn): FFN(\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "            (lin1): Linear(in_features=768, out_features=3072, bias=True)\n",
       "            (lin2): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (activation): GELUActivation()\n",
       "          )\n",
       "          (output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "  )\n",
       "  (pre_classifier): Linear(in_features=768, out_features=768, bias=True)\n",
       "  (classifier): Linear(in_features=768, out_features=6, bias=True)\n",
       "  (dropout): Dropout(p=0.2, inplace=False)\n",
       ")"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from transformers import AutoModelForSequenceClassification\n",
    "model_ckpt = 'distilbert-base-uncased'\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "# model = AutoModelForSequenceClassification.from_pretrained(model_ckpt)\n",
    "model = AutoModelForSequenceClassification.from_pretrained(model_ckpt, num_labels=num_classes).to(device)\n",
    "model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab86173b",
   "metadata": {},
   "source": [
    "## trainer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "8e16cf42",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-16T03:32:24.593980Z",
     "start_time": "2023-04-16T03:32:24.571630Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Token is valid.\n",
      "Your token has been saved in your configured git credential helpers (store).\n",
      "Your token has been saved to /home/whaow/.cache/huggingface/token\n",
      "Login successful\n"
     ]
    }
   ],
   "source": [
    "# https://huggingface.co/settings/tokens\n",
    "# (write)\n",
    "from huggingface_hub import notebook_login\n",
    "notebook_login()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "e516cccd",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-16T03:33:00.044487Z",
     "start_time": "2023-04-16T03:32:59.966149Z"
    }
   },
   "outputs": [],
   "source": [
    "# https://huggingface.co/docs/transformers/main_classes/trainer\n",
    "# https://huggingface.co/docs/transformers/v4.28.1/en/main_classes/trainer#transformers.TrainingArguments\n",
    "from transformers import TrainingArguments, Trainer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "28da7574",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-16T03:36:01.498100Z",
     "start_time": "2023-04-16T03:36:01.482836Z"
    }
   },
   "outputs": [],
   "source": [
    "batch_size = 64\n",
    "logging_steps = len(emotions_encoded['train']) // batch_size\n",
    "model_name = f'{model_ckpt}_emotion_ft_0416'\n",
    "training_args = TrainingArguments(output_dir=model_name, \n",
    "                                  num_train_epochs=4, \n",
    "                                  learning_rate=2e-5,\n",
    "                                  weight_decay=0.01,\n",
    "                                  per_device_train_batch_size=batch_size,\n",
    "                                  per_device_eval_batch_size=batch_size,\n",
    "                                  evaluation_strategy=\"epoch\",\n",
    "                                  disable_tqdm=False,\n",
    "                                  logging_steps=logging_steps,\n",
    "                                  # write\n",
    "                                  push_to_hub=True, \n",
    "                                  log_level=\"error\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d58c7ff8",
   "metadata": {},
   "source": [
    "- trainer默认自动开启 torch 的多gpu模式，\n",
    "    - `per_device_train_batch_size`: 这里是设置每个gpu上的样本数量，\n",
    "    - 一般来说，多gpu模式希望多个gpu的性能尽量接近，否则最终多gpu的速度由最慢的gpu决定，\n",
    "        - 比如快gpu 跑一个batch需要5秒，跑10个batch 50秒，慢的gpu跑一个batch 500秒，则快gpu还要等慢gpu跑完一个batch然后一起更新weights，速度反而更慢了。\n",
    "    - 同理 `per_device_eval_batch_size` 类似\n",
    "- `learning_rate`/`weight_decay`\n",
    "    - 默认使用AdamW的优化算法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "e8f5c638",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-16T03:36:42.908764Z",
     "start_time": "2023-04-16T03:36:37.358010Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Cloning https://huggingface.co/lanchunhui/distilbert-base-uncased_emotion_ft_0416 into local empty directory.\n"
     ]
    }
   ],
   "source": [
    "from transformers_utils import compute_classification_metrics\n",
    "trainer = Trainer(model=model, \n",
    "                  tokenizer=tokenizer,\n",
    "                  train_dataset=emotions_encoded['train'],\n",
    "                  eval_dataset=emotions_encoded['validation'],\n",
    "                  args=training_args, \n",
    "                  compute_metrics=compute_classification_metrics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "35d1eb9f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-16T03:38:54.930134Z",
     "start_time": "2023-04-16T03:37:44.376206Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/whaow/anaconda3/lib/python3.10/site-packages/transformers/optimization.py:391: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
      "  warnings.warn(\n",
      "/home/whaow/anaconda3/lib/python3.10/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.\n",
      "  warnings.warn('Was asked to gather along dimension 0, but all '\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='500' max='500' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [500/500 01:06, Epoch 4/4]\n",
       "    </div>\n",
       "    <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       " <tr style=\"text-align: left;\">\n",
       "      <th>Epoch</th>\n",
       "      <th>Training Loss</th>\n",
       "      <th>Validation Loss</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>F1</th>\n",
       "      <th>Precision</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>No log</td>\n",
       "      <td>0.426734</td>\n",
       "      <td>0.877000</td>\n",
       "      <td>0.867733</td>\n",
       "      <td>0.877837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.649800</td>\n",
       "      <td>0.212820</td>\n",
       "      <td>0.922000</td>\n",
       "      <td>0.921920</td>\n",
       "      <td>0.897502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.649800</td>\n",
       "      <td>0.188044</td>\n",
       "      <td>0.925000</td>\n",
       "      <td>0.925796</td>\n",
       "      <td>0.887741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.165300</td>\n",
       "      <td>0.171370</td>\n",
       "      <td>0.932500</td>\n",
       "      <td>0.932744</td>\n",
       "      <td>0.902770</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/whaow/anaconda3/lib/python3.10/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.\n",
      "  warnings.warn('Was asked to gather along dimension 0, but all '\n",
      "/home/whaow/anaconda3/lib/python3.10/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.\n",
      "  warnings.warn('Was asked to gather along dimension 0, but all '\n",
      "/home/whaow/anaconda3/lib/python3.10/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.\n",
      "  warnings.warn('Was asked to gather along dimension 0, but all '\n",
      "/home/whaow/anaconda3/lib/python3.10/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.\n",
      "  warnings.warn('Was asked to gather along dimension 0, but all '\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "TrainOutput(global_step=500, training_loss=0.4075119934082031, metrics={'train_runtime': 70.5276, 'train_samples_per_second': 907.446, 'train_steps_per_second': 7.089, 'total_flos': 1440685723392000.0, 'train_loss': 0.4075119934082031, 'epoch': 4.0})"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainer.train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "e2ec0dce",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-16T03:39:40.738636Z",
     "start_time": "2023-04-16T03:39:40.071296Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/whaow/anaconda3/lib/python3.10/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.\n",
      "  warnings.warn('Was asked to gather along dimension 0, but all '\n"
     ]
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "PredictionOutput(predictions=array([[ 5.321901  , -0.56112975, -0.5788935 , -0.86077183, -1.0296484 ,\n",
       "        -1.7686785 ],\n",
       "       [ 5.369429  , -0.719707  , -1.183184  , -0.56268054, -0.7231649 ,\n",
       "        -1.7755191 ],\n",
       "       [-0.9560261 ,  2.5084655 ,  3.2520792 , -1.2383951 , -1.5341177 ,\n",
       "        -1.6156932 ],\n",
       "       ...,\n",
       "       [-1.0118183 ,  5.2085857 , -0.22255814, -1.0138346 , -1.5563442 ,\n",
       "        -1.3893323 ],\n",
       "       [-1.3537518 ,  3.4339883 ,  2.6796649 , -1.1926656 , -1.7334485 ,\n",
       "        -1.4770639 ],\n",
       "       [-1.1767433 ,  5.1086726 , -0.15426004, -1.450834  , -1.5919915 ,\n",
       "        -0.62788427]], dtype=float32), label_ids=array([0, 0, 2, ..., 1, 1, 1]), metrics={'test_loss': 0.17136971652507782, 'test_accuracy': 0.9325, 'test_f1': 0.9327439172316551, 'test_precision': 0.9027697506235867, 'test_runtime': 0.6573, 'test_samples_per_second': 3042.753, 'test_steps_per_second': 24.342})"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preds_output = trainer.predict(emotions_encoded[\"validation\"])\n",
    "preds_output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "85a613f5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-16T03:40:17.390079Z",
     "start_time": "2023-04-16T03:40:16.717583Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/whaow/anaconda3/lib/python3.10/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.\n",
      "  warnings.warn('Was asked to gather along dimension 0, but all '\n"
     ]
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "preds_output = trainer.predict(emotions_encoded[\"validation\"])\n",
    "y_preds = np.argmax(preds_output.predictions, axis=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "eea593f9",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-16T03:40:30.101646Z",
     "start_time": "2023-04-16T03:40:30.092064Z"
    }
   },
   "outputs": [],
   "source": [
    "y_true = emotions_encoded['validation']['label']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "daaaeb93",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-16T03:40:47.575199Z",
     "start_time": "2023-04-16T03:40:47.565387Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['sadness', 'joy', 'love', 'anger', 'fear', 'surprise']"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "f78efee6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-16T03:40:50.101711Z",
     "start_time": "2023-04-16T03:40:49.779543Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from transformers_utils import plot_confusion_matrix\n",
    "plot_confusion_matrix(y_preds, y_true, labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "1f2ba808",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-16T03:41:59.283243Z",
     "start_time": "2023-04-16T03:41:58.345220Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/whaow/anaconda3/lib/python3.10/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.\n",
      "  warnings.warn('Was asked to gather along dimension 0, but all '\n"
     ]
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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hbGws/N64cWMMHTpUaflBgwYJr4ODgzXqiyLNmjVD3bp1AQDHjx9/5/rexYMHD9CvXz/hBt4xY8bg888/L1Bu3759uHfvHgCgQ4cOSofJSDk5OQn1ZGZmYtu2bXLLc3JysHbtWuH3efPmwcHBQWl9P/30EywsLNR7U2p49uwZtm7dKvy+cuXKIg8Dld0GJkyYgCZNmigt6+3tjbFjxwq/r1ixotD6PT09MW7cOKXLPTw84O3tDQAQi8W4evWqOt0udbLD2hwdHUu0rZLeLzk6Oqp8iGzFihXxwQcfCL8Xx35DVu/evdGuXTuly/MPuZs5c6bSz9zCwqLY+zpy5Ejh9bFjx965PmUmT56s1jBSTQwaNAjDhg0DINl3DRkyBMnJydi/f78wZFkkEuHff/+FnZ1dsbZNmuM9QESkse+++w5t2rQBAKxduxZff/01qlevrlEdJ0+eFF6PGjWq0PIjR47EjBkzkJOTg+fPn+Pu3buoU6eO0vKdO3eGra2tRn3y8/NDhQoVlC43MTFBjRo1hNnY+vbtq7I+Dw8P4XVkZKRafYiIiMCVK1fw4MEDJCQkID09Xe6gSnqPwOvXr/H06VNUrVpVrXqLU0JCAnr06CGM62/btq3Se5IOHjwovJZNCFVp37698PrcuXP44osvhN9v376NFy9eAJDcM1VYndbW1ujVqxc2btyoVtuFOXbsmDCjm7u7O7p27VqkepKSknDlyhXhd3W2gTFjxgiJT3BwMJKTk2Fubq60fP/+/Qut08vLC5cvXwYAuVmztImLi4uQRC9btkwu6ShuJb1f6tGjB0xMTFTW6eXlJST+xR0TTfZZANCnTx+1y6uzj8vMzMSlS5dw7do1vHjxAm/fvpWbIVH2nrqwsLBC6ysqdfdFmlq6dCnOnz+PBw8eICIiAqNGjZL7Tn311Vdy+zcqO0yAiEhjfn5+6NSpE44ePYrMzEx8++23WLdundrrP3v2DDExMcLv+W92VcTR0RG1atXCnTt3AEieTK/qQEPV2XRl6tevX2gZ2aSqXr16apct7ObsAwcO4JtvvkFoaGihfZCKjY0t9QQoOzsbgwYNwu3btwEANWvWxM6dO2FoaKiwvHQyCEDyHtU5qJEmeYDkBnhZsutLp64tTPPmzYstAQoKChJeqzuVsiLXrl0TJnCwsLBAgwYNCl2nUaNGMDc3R3JyMrKzs3Ht2jWV246np2ehdcpOYCD7uWuTgQMHClc8Z82ahcDAQPj7+6NTp04FpuB+F6WxXyrrmBS2j5PdZ1lbW6NKlSpql1e1j0tNTcUPP/yAlStXIjY2Vq2+qltOU4aGhmrFoSgsLS2xceNGtG7dGllZWXJXsBs3bozvvvuuRNolzTEBIqIi+e6774SZzTZs2IDp06cLw7MKIzvjlampqcqrLrLc3NyEA43C/jkWZaiMOgfTBgZ5u83CysuWVfUcmHnz5uHbb79Vo4fyZM+WlpYvvvgChw8fBiB5//v27VM5nEM6UxQA7N69W+P24uLi5H6X/e64uLioVUdxJokvX74UXmt61VOW7PuoWrWqWjNP6enpoWrVqmpvA+p8n2UTV219Hsno0aMRGBiIHTt2AABOnz6N06dPAwAqV64MX19ftG/fHh999NE7DZErjf1SWcdEk32WpvtDZX2Ni4tD+/btNb6iU1L7N1tbW7l+F7dmzZph3rx5mD17tvA3MzMzbNq0SemJIip9vAeIiIrEx8dHGB+fk5ODuXPnqr2u7MPuVA3hyU+2bGH/HE1NTdWuV0rdBzcWtbwiR48elUt+Wrdujb/++guhoaGIjY1FWloaxJIJayAWi4Whh4Dkcy9Nq1evxh9//AFAMu30tm3bVJ7tBt79DHb+xFH2u6Nsiuz8NPmOFUb2e/cu9xaVxjZQHN9PbaCnp4dt27YhICCgwJWy6OhobN26FePHj0flypUxZsyYIj+kWRdiokn7xdXXSZMmCcmPsbGx8HDge/fuCUPgpPs32WF0JbV/K8r/Bk3lvy+wfv36nPJayzABIqIiW7BggfBPcseOHWqf4ZM9cExOTla7PdmylpaWaq+nzX799Vfh9bhx43D27FmMGTMGjRo1gr29vdykC0DZXPUBJM+9mTx5svD74sWL0blz50LXkz04DAsLk0vm1PnJfw+E7HcnJSVFrb5r8h0rjOz3TtVT6wvDbSCPOge6IpEIw4cPx7Vr13D//n2sWbMGw4cPl7sKl5WVhTVr1sDHx0fuao66GJPi9+zZM2zZsgWA5KRJYGAgVq5ciR49eqBmzZqwsLCAvr6+UL6s9m/F6d69e5g6darc34KDg+X29VT2mAARUZE1aNBAuNFaLBbjm2++UWs92WEqqampao/1fvz4sfBa1cxf5UV2drYwlEdPT0+t8eHSh6aWpvv37wsPfAWAiRMnyiVDqsieCZXeyP4uZL876n4W+e8jehey70fdiS0UkX0fT58+LXT2MECSKMi+F23dBtQd+iml6VXCGjVqYNSoUQgICBBuNv/yyy+Fdh88eFCkIaXcLxW/EydOCN/t7t27w8/PT2V52c+yPJKd/Q2A3BXyb775RmtnWtRFTICI6J3Mnz9fOIO3f/9+4QnpqlSpUkVufP2FCxcKXef169e4e/eu8Hvjxo2L0FvtEhsbi4yMDABAhQoVCr1/ITw8vMRuDFZGOuObdFhRx44dhWFw6mjWrJnw+siRI+/cn0aNGgmv79y5o9bBs+zEBe+qefPmwmvZ2Z001bBhQ2G7SUpKwo0bNwpd59q1a8KBlb6+Pho2bFjk9kuS7FUQ6UyBymRkZCAiIuKd2nN3d8evv/6K+fPnC3/bu3evxvVwv1T8ZO8BVGeSmTNnzpRkd0rcnDlzhNkdnZyccPbsWWHq/8zMTPj7+6t95ZpKFhMgInontWvXlnsWjuyNn6rIPosiICCg0PLr1q0ThspUrlwZtWvX1qyjWkj2xvfU1NRCrwKo8+yX4pSdnY2BAwcKN3jXrl0b27dv1+gG4g8//FB4vWnTJrlZtoqibt26wlWYrKwsYXiNMgkJCdizZ887tSmrU6dOwvu/d+9ekZM6CwsLNG3aVPhdnW1A9vlHPj4+xXpvU3GqVq2a8LqwYbF79+5FWlpasbQr+12TnaxCE9wvFS/ZfVxhB/4pKSn4999/1apXdipxbZm84/Tp08Kz50QiEQICAuDg4IDly5cLsxXevXu3wPA4KhtMgIjonc2dO1eY3ebYsWPCsC5Vxo8fL7z+77//hJnFFHn69Knc8LDx48eX+c3ExcHe3h5WVlYAJAfqqj638+fPl3oCNHXqVOEA39bWFvv27YONjY1GdfTt2xc1a9YEIDnAGTp0qNoHLElJSQXuxdDT05N7WOK3336r8qrY9OnT3+lenfwqV64s97DI8ePHF/lgW3YbWLZsGa5fv660bGhoKFauXCn8PmHChCK1WRp8fHyE16qSiMTEREyfPr3Q+tS96ik7JLKos8Fxv1S8ZO/ROnDggMohkf/73//U3pZkpwp/9uxZ0TtYTOLj4/Hxxx8LyfCnn36KLl26AACsrKywYcMG4YrvX3/9VaQZMal4MQEiondWrVo1jB49WvhdnSFH7dq1Q7du3YTf+/fvj+3btxcoFxoaig4dOgjTIVetWhWffvppMfS67Onp6aF79+7C7yNHjhQeSilr27Zt6N69O7Kzs0vtrP+qVauwdOlSAJJ7Onbs2FGkWYz09fWxYsUK4Z//0aNH4efnp/Kp8devX8eMGTPg4uKi8D6bL774QjgAev78OTp37oz79+/LlcnIyMD06dOxcuVKGBkZadxvVX788Ueh/cePH6NFixZKrwTFx8dj9erVmDZtWoFl/v7+wjC2jIwMdOnSReGwuuPHj6Nr165C4ti4cWMMHjy4uN5OsRsyZIjwesuWLcL3SNadO3fQvn17PHjwoMBEH/m5uLhg3LhxOHXqlPDspPyCgoLk7kuT3a40wf1S8Wrfvr0wW+ODBw8wYsQIxMfHy5VJTEzEuHHjsHLlSrX3b7LP8ZF91k5ZGT9+vHB/noeHB3766Se55a1atZJ7gO+YMWPw/PnzUu0jyeNzgIioWMyePRsBAQEaDWdZu3YtWrVqhQcPHiApKQkDBgyAu7s7mjVrBiMjI9y+fRtBQUHC0DBzc3Ns3rxZ46sQ2uybb77Bnj17kJqaikePHqF58+Zo0aIFatWqhYyMDFy8eFFIAsaOHYuIiAi1rrC9K9l/4NWrV8euXbuwa9euQtdzd3fHZ599Jve3jh07YsWKFZg4cSKys7MRFBQEHx8fuLu7w8vLC7a2tkhNTcWLFy8QFhZW6DA5R0dH/PXXX+jfvz+ys7MRGhqKOnXqoE2bNqhevToSExNx8uRJvHr1CoaGhvjuu+8UJiBFVbVqVWzduhW9e/dGUlISIiMj0bVrV7i6usLHxwd2dnZISkpCREQEwsLCkJmZiV69ehWox8jICJs3b0abNm3w6tUrvHjxAu3bt0fDhg2Fe53CwsJw7do1YZ0KFSpg8+bNWv08EV9fX3zwwQc4cOAAAGDKlClYtmwZmjdvDpFIhLt37yIoKAg5OTkYMWIEIiMjVX6nU1NT8ddff+Gvv/6CpaUlGjVqBBcXF5ibmyM2NhZ37txBeHi4UN7R0RHz5s0rcv+5Xyo+tra2+PLLL4X7szZu3IhDhw6hWbNmqFKlCp4/f45Tp04hOTkZ+vr6WL58OYYPH15ovX379hWuzk2fPh2HDx9G/fr15ZLpWbNmyT2otaQEBAQISZixsTE2bdokN0RPas6cOTh69CiCgoLw+vVrDB8+HEeOHOFVw7IiJiJSoE2bNmIAYgDiFStWqLXO1KlThXWkP8OHD1e5zosXL8Tt27cvsF7+n5o1a4ovX76ssq7hw4cL5deuXatWn+fOnSusM3fu3ELLy34uJ0+eLLS87HtQZvfu3WIzMzOV73/cuHHitLQ0tdpX53NYu3atyhi5uroWGhNFP23atFH6Pk+cOCF2d3dXu6769euLnz17prS+TZs2iS0sLJSub2lpKd65c6f45MmTavVPU2FhYeKGDRuq9V78/f2V1nP37l2xl5dXoXU0btxYfP/+fZV90vT7qe73X53vsazXr1+LmzZtqvL9jBo1Sq3vtKoY5/9p2LCh+Pbt2wr7pMn3oCz3S4Vtm5qS3ZYjIyNVlo2MjBTKurq6Fktfs7KyxMOGDVP5OdrY2Ij/++8/tdvPzMwUt2vXTmWdsu9V0/clVdh35v79+3Lfz0WLFqms78GDB2JLS0uh/O+//652X6h4cQgcERWbGTNmaDxEq2LFijh+/DgOHz6MESNGCM+GMDY2RtWqVdGzZ0/8888/CA8Ph7e3dwn1vGz16tULN2/exOTJk1GrVi2YmJjAwsICtWrVwsiRI3H69GmsWrWq0KFC2q5du3a4c+cOduzYgZEjR6Ju3bqwtbWFvr4+LC0tUbNmTXz44Yf44YcfEBoaips3b6Jy5cpK6xs8eDDCw8Px5Zdfom7dujA3N4e1tTU8PDwwbdo0XLt2DX369Cmx99OwYUOEhoZi165dGDZsGNzd3WFlZQV9fX3Y2tqiSZMmmDhxIg4ePKjy5u5atWrhypUr2Lp1KwYMGAA3NzeYmZnBzMwMbm5uGDhwILZv344rV66gRo0aJfZ+ipOdnR0uXLiA5cuXw9fXF3Z2djAyMoKrqyv69euHwMBArFmzRq3v9OvXr3H06FHMnj0bXbp0QfXq1WFubi58b+rWrYuhQ4fiv//+Q0hISKEP51UH90vFR19fH+vWrcO+ffvQo0cPVKhQAYaGhqhQoQKaNm2KBQsW4NatW+jdu7fadRoYGODIkSNYuXIlOnXqBCcnp2If6lqYrKws+Pv7C/cYdu7cucDV7/yqV6+OP//8U/h95syZcld4qfSIxGI1Hj5ARERERET0HuAVICIiIiIi0hlMgIiIiIiISGcwASIiIiIiIp3BBIiIiIiIiHQGEyAiIiIiItIZTICIiIiIiEhnMAEiIiIiIiKdwQSIiIiIiIh0BhMgIiIiIiLSGUyAiIiIiIhIZzABIiIiIiIincEEiIiIiIiIdAYTICIiIiIi0hkGZd0BotKWlpaGGzduAAAcHR1hYMDNgIiIiKgsZWVl4dWrVwAAT09PmJiYlFhbPPIjnXPjxg34+PiUdTeIiIiISIHLly/D29u7xOrnEDgiIiIiItIZvAJEOsfR0VF4beTeDyJD8zLsDSlyfd+Csu4CKaCnJyrrLpASOTnisu4CUbnC/Zn2ef78OfxaSkboyB6rlQQmQKRzZO/5ERmaQ2RkUYa9IUWqODuXdRdIAX0eMGitbCZARBrh/ky7lfT92RwCR0REREREOoMJEBERERER6QwmQEREREREpDOYABERERERkc5gAkRERERERDqDCRAREREREekMJkBERERERKQzmAAREREREZHOYAJEREREREQ6gwkQERERERHpDCZARERERESkM5gAERERERGRzmACREREREREOoMJEBERERER6QwmQEREREREpDOYABERERERkc5gAkRERERERDqDCRAREREREekMJkBERERERKQzmAAREREREZHOYAJEREREREQ6gwkQERERERHpDCZARERERESkM5gAERERERGRzmACREREREREOoMJEBERERER6QwmQEREREREpDOYABERERERkc5gAkRERERERDqDCRAREREREekMJkBERERERKQzmAAREREREZHOYAJEREREREQ6gwkQERERERHpDCZARERERESkM5gAERERERGRzjAo6w4Q6QLnirYYN6gtOreqD2cnW2RkZOFh1CvsPhaKNdvPIDU9853bqF+zMsYMaIPWjd1RqYI1srNz8CwmHkfP3cRf284g6mWc2nWZmRhhSI/m+LBdQ7i7VoS9jTkS3qbi+asEXLr2EIfP3sDJS3feuc9lLerFG6zeehpHL9zCsxdxMDIyQDVnB/Tq0Bij+vnCzMSoWNrZFXgVm/cH4db9aCS8TUEFeys0b1gDo/v5oqlnNZXrJrxNQWj4E4SEP0Zo+GOEhD/Gy9hEAEDLxjWxd8VnxdJHbfP0xRus3nIKgedvISo3NtWdHdG7oxdG9/crttjsDLyCTfuCcOteNOJzY9OiUQ2M6e8H70JiI/UmPgmrtp7GgdPX8ST6NQDApbI9PmjTAOMHtoGdjUWx9FVbcLvRToyL9uL+TPuIxGKxuKw7QVSaoqKiULVqVQCAcb3hEBmV7MbcuVV9rF4wHNaWZgqXRzx6gQFTV+Dxs9dFbmP6uO74anRX6OkpvqibmJSKT75djwOnrhdaV+sm7lg2ZyhcKtsrLXMjIgp+/j8Vub+Fib30Z4nVLRV47iYmzP0XiUmpCpfXdK2ALYsmwq2KQ5HbSEvPxKiZ/yDw3E2Fy/X0RJg2pju+HN1VaR2Ne8/Fk+dvFC4r7QMGfT1RqbRz5NxNjPtmndLYuLtWxLbFE+Hm/G6xGTF9DY6oiM30sd3x1ZhuKusJufUY/l+uwovcg7j8KjlaY+Nv4+BVz7XIfVVHdk7p/CvndqOdGBfNcX9WUFnvz6KiouBeTXJ89vTpUzg7O5dIOwCvAGk1Nzc3PH78GMOHD0dAQEBZd4eKwMO9Cv75cRTMTY3xNjkNiwMCcfbqPZgYG6JP5yYY8VEr1HJzwtZFE9Fh+C9ITs3QuI2pwzvh67HdAQDPXyXgz/XHcPn6QwCAT4Pq+HRYRzg5WOPv70ai1yd/4PL1SKV1tfGpjc2/j4epiRESk1KxbvcFnAm+i5jXiXCwtYRLJTt0bFkPjnZWRftAtMTNiCiMmbUWKWkZMDczxtThndC6SS2kpmfgv8AQrN9zAfcfx2DwFytxdO1XsDAzLlI7n32/SThYaN3EHeMGtoWTozVu34/G4nWBiIyKxU+rD6CigxU+7tVSYR2yh7UV7CzRqJ6r0gOQ98GNiCiMmvEPUtIyYGFmjKkjOsO3iTvS0jOxK/Aq1u2+gHuPX2Lg5ytwfN20IsdmyoKNwsGCb9NamDCoLZwcrBH+IBoL1x5BZFQsflh1ABUdrDGst+LYRMfEY/AXKxHz5i0M9PXwiX97dGntAUBy0LN84wk8f5WAQZ+vxMn1X6NyBZsi9VVbcLvRToyL9uL+THsxASIqQT980RfmpsbIzMpG3ynLEHwjL/k4eyUCD5/EYP5nH6F2NSd84t8ev/59WKP6KzlaC8lPdEw8Ogz/FS9iE4TlV289xu5jITgW8BUqV7DBb18PRJuhP0PRhV97Gwus+X4kTE2McOfhc/SdsgzRMfEFygX8dx6GBvoa9VPbzFq0CylpGTDQ18OOPybJDQ3wa1ob1as64tule3Dv0Uus2HSi0LNmipwPuYedR64AALr4euDfn8dCX19yha5xPVd09fNEh+G/IOpFHL5dugc92zdSeJVwdD8/uFa2h1c9Fzg72QEAHJpNKcrbLhdmLtwpxGbnn5Pg06C6sMzPuzaqu1TA3D92I+LRSyzfeALTxhYhNlfvYUdubLr6emDDr+PyYlPfFd38PNH2458R9SIOc//cjV4dFMfmu+X7EPPmLQDgr+9GoHfHxsKyll414VXXBSNn/IOYN2/xw8r9WDpnqMZ91SbcbrQT46K9uD/TXpwEgaiEeNVzgW/TWgCADXsuyiU/Uks3nsCdh88BABMHtYOBvmabZJ/OTWBibAgA+Gn1AbnkR+r5qwT8tPoAAMCzljM6taynsK45k3rC3sYCaemZ+HjaXwqTH6nMrGyN+qlNQsIf43zIPQCAf88WCsdFT/Jvj1puTgCAVVtPFen9/rn+GABAX18Pv341QPiHJGVvY4E5k3oBAOITU7Bh70WF9Uwe2gE92jcSDhbeZyG3HuPcVUlshvZqIXewIDXZvz1qV5PEZsWWk0WKzRKZ2Pz29UCFsZk3JS826/cUjE3M60RsOxwMAOjQvK7cwYJU746N0aF5XQDAloOXEfNa8bCS8oDbjXZiXLQX92fajQkQUQn5oE1D4fXGfYr/GYjFYmw9eBkAYGttjtZNamnURmOZcbjHLoQrLXf84m3hdc8OXgWWW1mYol/XpgCAnYFXcf9xjEb9KE8Onc67D2rIh80VltHT08PA7t4AJP8wzuf+E1NXUko6zl6JAAC09amNyhVtFZb7sF1DWJqbAAAOnLqmURvvowOn8z4D/x4tFJaRxMYHgCQ2565GaNRGUko6zgTfBQC09amDKkpi06NdIyE2+04WjM2hMzeQnZ0DABjSQ/H3CAAG92gGAMjOzsGhMzc06qs24XajnRgX7cX9mXZjAkRUQlo0qgFAsoMKu/NUabnzIfeF180bFTxDpIqNVd5lbOmla0Vkz9S0alyzwPJuvh7CLDR7joUIf7cwM0b1qo5wsH0/Zn0BgIthDwAA5qZGaFinqtJyLRu7C6+Drj3QqI2Q8EdIz8iS1ONV8POWMjI0QFMPN8k6tx6X6ytrxUE2No1UxEb2OxwU9lCjNkJu5cVG0bYgZWRoAG9PN2Gd/LGR9lVSjzuUafUO3yNtwu1GOzEu2ov7M+3GBIiohNSqVhEAEBn1SjizokjEoxd56+QOU1BXisykCVYWpkrLyS5zqWQH09xhc1KyU5devhGJDi3q4vDfn+Pp6d9xdddc3Av8CeEHv8d3U/vAztpcoz5qm3uPXgIAqjk7wkDFvUzurhULrKN2G5F55d3dKqoomddOVnYOHj55f6+8qSMiUr3YyG4nstuPOu5Gym5vhcVG0k5Wdg4e5ItNRG49VhamqOigfFIQJwdr4cxrRKRm3yNtwu1GOzEu2ov7M+2m0wlQdHQ0pk+fjsaNG8Pa2hpGRkZwcnKCp6cnBg8ejICAACQmyo9xTE5OxtatWzFmzBg0atQI1tbWMDQ0hKOjI9q0aYPffvsNSUlJarV/8OBBdOvWDY6OjjAzM0OtWrXwxRdfIDo6utB1AwICIBKJIBKJ8OjRI+Tk5GD16tVo2bIlbG1tYW5ujgYNGuD7779HSkpKofWJxWLs2LEDffv2RdWqVWFiYgJbW1v4+PhgwYIFiI+PV7l+REQEpkyZAg8PD1hYWMDIyAiVK1dGo0aNMGrUKGzduhXp6ekF1svOzkZAQAC6dOkCJycnGBkZwcbGBu7u7ujQoQN++OEHhIcrH9qlrYyNDOBgawkAiH4Zr7JswttUJKVIPhtll6+Vkd1Zqjr701JmmZ6eXoEhDNIxyAlvUzCsV0vs+GMSmjWsIVemkqM1Jvm3x5mN04Xy5U1aeiZex0u2z0qFzGBjY2UGc1PJVbFnGjxDCQCexeSVr1RBdUxlY/FMxX1X7zvZ2FSuaKOy7DvFRmZ7LGwWoyoy/cjfjvR3dWZCkm7XmvZVW3C70U6Mi/bi/kz76ewscGfPnsWHH35YIMF5+fIlXr58iZs3b2LLli1wcHDAhx9+KCz/4IMPcPr06QL1xcbG4syZMzhz5gyWL1+OgwcPok6dOkrbnzp1KpYsWSL3t3v37mHRokXYuHEjDh48qPZ7SU5ORqdOnXDixAm5v9+4cQM3btzA3r17ceLECZibKz5z/+rVK3z00Uc4f/683N/T09MRHByM4OBgLFu2DHv27EGzZs0KrL99+3YMHToUGRnyUzg/f/4cz58/x7Vr17B27VrcuHEDHh4ewvKkpCR0794dZ8+elVsvISEBCQkJuH//Pk6cOIGQkBDs2LFD7c9DG1iYmQivk1MLJn75paSmw8LMWOMpMA+evo4vRnYBAEwb0w1Hz98SLodLGRsZYFq+WX/yt2ObO5TOxNgQ86b0Qlp6Jn5afQDbDgUjNi4JNVwc8enHnTD4w2aoUtEWG38bhzZDfyrStN1lKSklTXhtblr4Z21mYozk1Ay1YijXTnJeeQtT1Q+4M5NZnpyiWTvvE41jYyqJTZKGn5lcO4Vsb2Yy/cj/HZC2q842Kz240fR7pC243WgnxkV7cX+m/XQyAUpPT8egQYOQmJgIS0tLTJw4Ee3atUOFChWQmZmJx48f4+LFi9i5c2eBdbOysuDp6YmePXuiadOmqFy5MsRiMR4/foz//vsP27ZtQ2RkJHr37o2wsDCYmJgUqOP3338Xkp/KlStjxowZ8PHxQVpaGg4cOIDFixejX79+al25AYBx48YhKCgIw4cPx4ABA+Dk5IQnT57gl19+wcWLF3H58mV89913+PHHHwusm5ycjDZt2uD27dswMjLCyJEj0b17d1StWhXJyck4c+YMFi5ciJcvX6Jbt24IDQ2Fq2vejfcvX77EyJEjkZGRgQoVKmDy5Mlo3rw5HBwckJaWhocPH+LMmTPYtWtXgbbnzZsnJD8ffvgh/P394eLiAhMTE7x69QrXrl3D/v37IRKVzsPKipOJcd6mlZGZpaKkfBmTfEPTCnP11mMcPH0d3ds0gGctZ+xfNRULlu9FcO6zfrwbVMM3n/SEZy1npGdkwthIUn/+IXDSHaN0+YS5a7HneKiw/M7DF/jk2/VIz8jEiD6tUcOlAkb29cXSDcc16m9Zk00OjQwLn8rbyEgSx7T0TA3byStvaKh6N2ssszxVw3beJ2npsrEp/F+TtIzmsZFpx6CQ2BjJxCZNvp203BgbavA9Kq/x5XajnRgX7cX9mfbTyQTo/PnzwjCzTZs2yV3hAYBmzZphwIAB+PXXXwskIWvXroW7e8EbxKTrjB49Gl26dMHdu3exceNGjB49Wq7cy5cvMWfOHACAq6srgoKC4OSUN5zIz88PXbp0QZcuXZCVVfiBMwBcuHAB69evx9CheXOyN27cGN26dUPTpk1x8+ZN/PXXX1iwYAEM8m0g06dPx+3bt2FtbY1jx46hadOmcstbt24Nf39/tGjRAs+fP8fs2bOxfv16YfmBAweQnJwMADh+/LjcFR4AaNGiBfz9/bFkyZICz57Ztm0bAKBfv37Yvn17gffVpUsXTJs2DW/eKH5qtDJRUVEqlz9//lyj+oqitHZ+ADBx3npsX/IJfBpUQ1MPN+xZ/mmBMldvPcLtB88xtKdkJpr8Z5lk/8Fdvh4pl/zImr98HwZ90Awmxobo27lJuUuAZP8BZGQWfoNuRkbRElNpIgkAmYUkwOkyy/MnprqktE4ayH0HCtnHyh5cmJrIt2NiZIiUtAxkavA9Kq/x5XajnRgX7cX9mfbTyXuAXrzIu2/Cz89PaTkDAwNYWcnfDKYo+ZHVsWNH9OzZEwCwe/fuAsvXrVsnJFW///67XPIj1b59e4wdO1ZlO7L69Okjl/xIGRsbY/LkyQCA169fF7iXJjY2Fn///TcAYP78+QWSHylXV1d88803AICtW7fKJYXSz9LW1rZA8iPLxMQEpqbyN+lL1/X19VX5/uzsNHteQNWqVVX++Pj4aFRfURTl8rdkPc0vKScmpeLD8YsxY+EO3H4on9y9iE3AL38fQvexi4UbFwHJdJuy3soMcTh28ZbStuISkhF2+wkAwMO9isbPLSprGg9NTJOUUSeGcu2Y55VPKmSYoOxEFoUNYXifFWXYqGQ9DWMj204h21uKTD/yfwek7aqzzUqHimr6PdIW3G60E+Oivbg/037l6+ilmFSqVEl4vXbt2neq69WrV7h37x5u3rwp/Dg6OgIArl0rONf6sWOSB1bZ2tqiV69eSusdNWqU2n3w9/dXuqxJkybC64cP5adXPHLkCNLSJAfqAwYMUNmGNFHMzMzE1atXhb9LP8u4uDjs2bNH7T7Lrps/qXofpGdkITZOMi11YTdAWluaCjufot5UmJmVjZWbT6HlwO/h2vZLNOnzLep0nYm63Wbhx1UHkJGZhfo1qwCQJEz5b06VbbewSRukZQ0M9GFnU75mhDMxNoS9jWRK7+eF3KAbn5gi7Og1nZxC9kbS5zGqYxot89lXUeMG1PeVbGwK+w6+U2xktkdVD/sF5G8wzt+OtJ7C6pDUE6ewjvKC2412Yly0F/dn2k8nE6DWrVujenXJ81amTp0KHx8f/Pjjj7hw4UKBG/kVOX/+PAYOHAh7e3tUqFABtWrVgqenp/Dz119/AZBcYcnvxg3Jg6O8vLwKDEeT1ahRIxgZqb7ZUErVZAuyV0/evpV/TsyVK1eE15UqVRJmlVP0I3t1R/YKWs+ePWFjYwMA+Oijj9C+fXssWrQIV69eRXa26kupw4cPByAZwletWjVMnjwZ//33H169elX4m1bh6dOnKn8uX778TvWrS3YKzPxPZpb1LlNgKpKYnIaHT1/hpcyzfxztLFG9qgMAIPT2kwLDEe/IXDlS1VdAMoucVFaW8um9tZV0GtfIqFfIUvGcinuP1Z/6Nb9aMrPkFTblrLQdA309VKvqqFE77xvZqeNVxeZdpo6vU012eyssNpJ2DPT1UD1fbGpXk5zASUxKxctY5U9EfxGbgLfJkhNN0vdXHnG70U6Mi/bi/ky76WQCZGhoiH379qFu3boAgODgYMycOROtWrWCjY0NunXrhk2bNik8gJ83bx5at26Nbdu2FXpvSmpqaoG/xcVJMucKFSqoXNfAwEDtoV9mZmZKl8kerOZ/PzExRZujX/Zqjb29Pfbu3YsqVapALBbj5MmT+OKLL9C0aVPY2dmhb9++2L9/v8J6vvnmG4waNQoikQgxMTFYtmwZ+vTpg4oVK8LT0xNz587Fy5eazzPv7Oys8kf2CmBJkj4kzMLMWO2HoF26ptlD0NTVt0sT4buw+2hIgeUXQvMexlrN2UFlXdLlKWkZiEssf1fumjeUnPxITs3ANRUPqL0Qkve09GYNNHtArVddV+G+LtnPNr+MzCxcuflIsk49V7XuF3ufNc+dej05NUPthwc3a6hhbGQ+Z9l68svIzELwjUcF1snra16752W+KwX7WvTvkTbhdqOdGBftxf2ZdtPJBAgA6tWrhxs3buC///7DqFGjUKOG5IuampqKw4cPw9/fH82aNZNLEo4fP45vv/0WAFC9enUsX74c169fR3x8PLKysiAWiyEWi4X7ZVRRZ2az/Gfpi5s0ITIyMhKmzFbnJ//QPV9fX9y/fx8bNmzAkCFD4OzsDABITEzErl270KNHD3Tt2rXAMDdDQ0OsWbMGN2/exOzZs9GyZUsYGRlBLBbj5s2bmD9/PmrWrKnx0DptceDUdeG1f48WCsuIRCIM7C65Jyk+MQVnr0QUez8szU3w2bBOQhs7jlwpUOZC6H28eiO5Qti9TQOldblUtodnLUl8L19/WOLf0ZIg+/427Q9SWCYnJwdbDwYDkAxRbN20lkZtWJqbwC93ndOX78oNC5G1/+Q14Wyaqs9dV3zQNu8z2LjvosIykthIruJaW5rCtyix8ZbG5o7SYaf7ToYJsfmwXcMCy7v5NYCenmQ/vmmf4u8RAGzedwkAoKcnQje/8htjbjfaiXHRXtyfaTedTYAAQF9fH71798aaNWtw//59REdHY82aNcJ9M1evXsX48eOF8tKhbTY2Nrh48SImTpwIT09PWFtbQ18/b+pA6VUeRWxtJWMmC7uykZWVpbKe4mBvbw8AyMjIgL29PTw8PNT6kQ55k2ViYgJ/f39s3LgRT58+xYMHD/DHH3+gVi3JhnnkyBHMmjVLYT/q1auHBQsW4Pz584iPj8fRo0cxcuRI6OvrIykpCYMHDy6VmduKW0j4Y1zIPSMztFcLeHtWK1Bmsn971KkuuSK1csspZGXLDykb/GEzxAUvRVzwUnw9trvCdiraWymdjMDCzBjrfh4DJwdrAMA3S/5TeJNjTo5YmNGtlpsTpg7vVKCMgb4efv96gDBEbu3Ocwrb1HaN67uheSPJCY+Ney8i+EZkgTLLNp4QhiWMG9gWhvme4r15fxAcmk2BQ7Mp+Pkvxc/s+mRoewCSp25P+3U7svPF9nV8EuYvkyT31pam+LhXy3d7Y++BJvXd0MJLEpsNey7i8vWCV0SXbjwhPP18wqCCsdm0Lwi23pNh6z0ZP60+oLCdKUM7AJDE5qtftimMzbw/82IzTEFsKjpYoX9XbwDA8aDbCmdO3H0sBMeDbgMABnbzUfmEdW3H7UY7MS7ai/sz7abb1yfzqVSpEkaNGoWPP/4YzZs3R0hICPbv34/U1FSYmpri1i3J7Fjt27dXOYRN9t6a/Dw9PfHixQuEhYUhKytL6X1A165dU+t+pHfh5eUlvA4MDBTuySkO1atXx5QpUzB8+HDUr18fUVFR2LZtGxYtWqRyPVNTU3Ts2BEdO3aEp6cnvvjiC6SmpmL//v0azYynLab/vgOH13wBMxMj7PxzEhYFBOLslQhhGukRfVoDkIybXraxaFNK9+/mjclDO2DL/ks4H3IPL2ITYWlhAm8PN4zu54eqlSRDKTfsvYgNexWfhQKAVVtP4aNOjdGorgvmTu6FOtUr5T4I9S2qV3XEJ0PaC0lc4Lmb2HsirEj91QY/fNEXH4xdhNT0TPT7dBk+H94ZrZu4IzU9E/8dDcG/uyUPBa7hUgGfDGlfpDb8mtbGR50a47+jITh89gb6TlmK8YPawcnRGrfvR2NRwBFEvZCc5Pjmk56wsVI8lPVGRBRuRiie2j3mdSI25zvr26O9l8YzCWmTn/7XD11HL0Rqeib6TlmGz0d0hm/TWkhLz8TOwKtY958kNjVdKmCSf4citeHnXRt9OjfBrsCrOHTmBj6avBQTBrVFJUdrhN+Pxu9r82Izd3IvpbGZPbEHjl8MR2xcEsbMWovQ8Cfo4iu5X/LI2ZvCNu1ga4FZEz9UWEd5wu1GOzEu2ov7M+3FBEgBQ0NDtGnTBiEhIcjKykJ8fDxMTU2F5/KomrEsLCwMQUHKLx927NgRR48exZs3b7Bv3z589NFHCsv9888/7/Ym1NCtWzcYGhoiMzMTixYtgr+/v8qJGYrCysoK3t7eiIqKUjgphCodOuTtDDRdV1vciIjC6Jn/YNX84bCyMMWcST0LlLn3+CUGTl1RpCmwpSraW+Gz4Z3wmYIrN5lZ2Vi28TjmL9unso70jCwM+nwlNi+cAK96LhjY3UcYnicr8NxNjJ71brMnlrUGtavir+9HYuLcf/E2OQ3frSj42dRwqYDNCyfITR+uqT9m++NtchqOXQjHuav3cO6q/NhqPT0R/jeqq5AIK3Lw9HX8+vchhcvuP47BlAUb5f7WqrF7uT5gaFC7Ktb8MArj56zD2+Q0LFheMDY1XSpg6+KJ7xSbpd9IYnP0/C2cvRJRYPipnp4IX43uipEqYuPsZIvNCydg6Jer8fJ1Ipb8exRL/j0qV6aivRU2/Dau3M+YBHC70VaMi/bi/kx76WQCdPbsWVSqVAk1a9ZUuDwjIwOnT58GAFhYWAjTWru7u+POnTs4d+4cHj58KMwkJ/Xq1SuFz+ORNXz4cMybNw+pqan44osv0LJlS1SsKD+TxunTp7F69eqivj21ValSBSNHjsTq1atx7do1jB8/HqtWrVKaBMXExGDv3r0YM2aM8LcjR46gQYMGSicWSEhIEGZdq1YtbwjYmzdvcO7cOfTo0UPp/VCBgYHCa9l1y5vDZ2+i9eAfMH5QO3RuXR+VK9ggMzMbD6NeYc+xUPy17fQ7PVF538kwGBsZwK9pLVRzdoSDrQUyMrMQHROPE0F3sGHvBdx5qN7sci9fJ6LTqN/wcc8W6NulKWpXc4K1pSneJCQj5NZjbNofJHdvU3nW1dcTZzbOwKqtp3D0/C08j4mHoaE+qjk7omcHL4zp7wczE/VmYlTG1MQIWxZNxI4jV7Bl/yXcuvcMCUmpcLSzRPNGNTCmv5/CoZG6rpufJ85tnolVW04i8NwtROfGprqzI3p19MLYAW2KJTbbFk/E9sPB2Lz/Em7ee4aEt5LYtGhUA2MH+MFHjZt8m3q44fyWmVi5+RQOnL6Gp88lk+O4VLZHd78GmDi4Lexyp8N9H3C70U6Mi/bi/kw7icTl8S7mdzRv3jwsWLAAvr6++OCDD9CgQQM4OjoiNTUVERERWLlypXDQPnXqVGHY1o4dO9C/f38AkpnGvv76azRp0gRisRgXLlzAwoUL8eLFCzRv3hwXL0qGGin6eH///Xd8+eWXACRJyIwZM+Dj44O0tDQcPHgQixYtgpOTE1JSUvDq1SsMHz4cAQEBcnUEBARg5MiRAIDIyEi4ubkpfK+PHj0Skoe1a9dixIgRcsuTkpLQokUL3Lx5E4Dkfpxx48ahSZMmsLCwQHx8PG7duoVjx47h4MGD8PT0lBviN2LECGzatAmdOnVC586d4eHhATs7O7x9+xY3b97E0qVLcfu2ZMzo4sWL8dlnn8n1y83NDX369EGzZs3g6uoKAwMDPH/+HPv27cPff/+NnJwcODs7486dOzA3L55nzkRFRaFqVcmsbMb1hkNk9H5szO+T2Et/lnUXSAF9vcInb6GykZ2jc//Kid4J92faJyoqCu7VJMdnT58+FSbVKgk6eQUIkMy8cfr0aeFKjyJ9+vTBjz/+KPzer18/jBw5EmvXrkVUVBSmTJkiV15fXx+LFi1CXFyckAAp8r///Q9PnjzBH3/8gWfPnmHy5Mlyyx0cHLBjxw7069eviO9OfRYWFjh9+jT8/f1x+PBhhIeHY+rUqUrLW1kVvOktMzMTBw8exMGDim+eBIBJkyYV+LwASSK0cOFCpetVqVIFe/fuLbbkh4iIiIh0m04mQNOmTUOzZs1w9OhRXLx4EdHR0cJ0105OTmjWrBmGDRuG7t0Lzrr1zz//oH379li9ejXCwsKQkZEBJycn+Pn5YfLkyfDx8cG8efMK7cOSJUvQpUsX/PHHHwgODkZKSgqcnZ3RvXt3fPXVVyWa9eZnZ2eHQ4cO4cSJE9iwYQPOnTuH58+fIy0tDVZWVqhRowZ8fHzwwQcfoHPnznLrLl68GD179sTRo0dx5coVPH/+HK9evYK+vj6qVq2Kli1bYsyYMWjVqpXceq6urggLC8PRo0dx4sQJPHz4EC9fvkRSUhJsbGxQv3599OjRA+PGjYOlpWWpfRZERERE9H7TySFwpNs4BE77cQicduKQEe3FIXBEmuH+TPuU5hA4nX4OEBERERER6RYmQEREREREpDOYABERERERkc5gAkRERERERDqDCRAREREREekMJkBERERERKQzmAAREREREZHOYAJEREREREQ6gwkQERERERHpDCZARERERESkM5gAERERERGRzmACREREREREOoMJEBERERER6QwmQEREREREpDOYABERERERkc5gAkRERERERDqDCRAREREREekMJkBERERERKQzmAAREREREZHOYAJEREREREQ6gwkQERERERHpDCZARERERESkM5gAERERERGRzmACREREREREOoMJEBERERER6QwmQEREREREpDOYABERERERkc5gAkRERERERDqDCRAREREREekMJkBERERERKQzmAAREREREZHOYAJEREREREQ6gwkQERERERHpDCZARERERESkM5gAERERERGRzmACREREREREOoMJEBERERER6QwmQEREREREpDMMyroDRGXpxv4FcHZ2LutuUD52vl+XdRdIgahjP5R1F0gJM2P9su4CKSESicq6C6SAWCwu6y5QPqUZE14BIiIiIiIincEEiIiIiIiIdAYTICIiIiIi0hlMgIiIiIiISGcwASIiIiIiIp3BBIiIiIiIiHQGEyAiIiIiItIZTICIiIiIiEhnMAEiIiIiIiKdwQSIiIiIiIh0BhMgIiIiIiLSGUyAiIiIiIhIZzABIiIiIiIincEEiIiIiIiIdAYTICIiIiIi0hlMgIiIiIiISGcwASIiIiIiIp3BBIiIiIiIiHQGEyAiIiIiItIZTICIiIiIiEhnMAEiIiIiIiKdwQSIiIiIiIh0BhMgIiIiIiLSGUyAiIiIiIhIZzABIiIiIiIincEEiIiIiIiIdAYTICIiIiIi0hlMgIiIiIiISGcwASIiIiIiIp3BBIiIiIiIiHQGEyAiIiIiItIZTICIiIiIiEhnMAEiIiIiIiKdwQSIiIiIiIh0BhMgIiIiIiLSGUyAiIiIiIhIZzABIiIiIiIincEEiIiIiIiIdAYTICIiIiIi0hkGZd0BIl0Q9eINVm09jcDzt/DsRRyMjAxQ3dkBvTo2xuh+vjAzMSqWdnYGXsWmfUEIvx+N+LcpqGBvhRaNamB0P194e1ZTq4438clYte0UDp66jifP30AMwLWSHbq3bYDxA9rCzsa8WPqqDZwr2mBcv1bo3KIOnCvaIiMzCw+jYrH75HWs2XURqemZ79xG/RpOGNO3FVp7VUclB2tk5+TgWUw8jl64g792XUDUy3il6xoZ6qNTizpoUrcqvOpWhXNFG9hbm8PCzBhvk9Nw/8krnLpyH+v2XkL0q4R37qs2efYyDv/sOIPjF2/h2ct4GBkawM3ZAT3aNcLwj1rDtJi2mT3HQrDt4CXcfvAcCUkpcLSzhE+DGhjepzWa1HdTuW7z/t8i6kVcoW04O9kiaPvcYumvNuD+TDs9ffEGq7ecQuD5W4gS4uKI3h29MLq/XzHG5Qo27QvCrXvycRnT30+DuCRh1dbTOHD6Op5EvwYAuFS2xwdtGmD8wDaws7Eolr5qC24z2kckFovFZd0JotIUFRWFqlWrAgAiHj6Bs7NzibYXeO4mxs35F4lJqQqXu7tWwNbFE+FWxaHIbaSlZ2LkjH9w5NxNhcv19ET4emx3fDW6q8p6QsIfY+iXq/EiNlHh8kqO1tjw6zh41XMpcl/VYef7dYnWDwCdW9TB6jmDYW1pqnB5xOMYDPjqHzyOflPkNqaP6oSvRnSAnp7ii+2JSan45IdtOHDmlsLl1arYI2Rr4Z9FUko6vlz4H7YeDilyX9URdeyHEq1f6viFW5iyYD0Sk9IULq/hUgH//joOrpXfbZuZMCcAxy4o/uz19ET4YmRXTB3RRWkd2pQAmRnrl2j9UtyfaU4kEpVo/QBw5NxNjPtmnYq4VMS2xRPh5vxucRkxfY3KuEwf2x1fjemmsp6QW4/h/+UqlXHZ+Ns4eNVzLXJf1VFah7/cZtQXFRWFWtUldT99+rREj8+YAFEBAQEBGDlyJAAgMjISbm5uZduhYlaaCdDNiCh0HbMIKWkZsDAzxtThndC6SS2kpWdg19EQ/Lv7AgDA3a0ijgd8BQsz4yK1M+6bddhx5AoAwLeJO8YPagsnR2uE34/GooBAREbFAgAWzxyMYb1bKqwjOiYe7Yf9gpg3b2Ggr4dPhrRHl9b1AQBHzt3C8k0nkJWdgwp2ljjx7zRUrmBTpL6qo6QTII+alXB4xSSYmxrhbUo6Fq8/gbMhD2BibIg+HRthRM9mAIC7j16iw9g/kZyaoXEbU4e2xdwJ3QEAz2MT8eem07h88xEAwMfDDZ8OaQMnByukpWei12ercfnm4wJ1VKtij8MrPsG5kAcIuROFpy/i8PJ1IjKzclDJ0QqdW9RF/85eMDU2RE5ODgZOW4tjQXeL/sEUojQSoPD7z9Br4hKkpmXA3NQYk4d2RMvGNZGWnok9x0Oxad9FAEBN1wo4sPp/MC/iNjN5/nrsPnoVANCysTtG9/NDRQdr3HkYjT/XH8PjZ5Jt5pdpAzGkRwuFdUgToC6tPfDV2A+UtmVkoI/qLhWK1E91lUYCxP1Z0ZR0AnQjIgpdRy/Mi8uIzvBt4o609EzsCryKdblxqeVWEcfXTStyXMbODsiLS9NamDCoLZwcrBH+IBoL1x4R4rJk1hCVcWn38c95cfFvjy6tPQBIkrjlG/PicnL91yUal9I4/OU2oxkmQFSmmAAVn54T/8C5q/dgoK+H/aumwqeB/CXoP9Yfw7w/9wAApo/rjmmFnDlT5HzIPfSY8AcAoKuvB9b/Mhb6+nlXHF7HJ6HdsF8Q9SIONlZmCP1vLqwtzQrUM+nb9dh84DIA4J8fRqF3Ry+55buPhWLUzH8AAEN6NMfSb/w17qu6SjoB2vvHePg2roHMrGx8MGkFgm89kVs+ZXAbzJ8kOaD94e9A/BpwTKP6KzlYIWTr1zAxNkT0qwR0GPMnXrxOLFDm2F9TUNnRGjfuRaPNqCUF/iGLRKJC/0k3rlsVh5ZPhJGhAcLuRqHd6D806qsmSiMB6v/pUlwMvQ8DfT3sXDoFTTzkt5kVm07g+xV7AQD/G9UVn49UfUZTkYuh99H/06UAgE6t6uPv70fLbTNv4pPQbczvePYyDtaWZriwdbbCbUaaAPXv6o1Fs0pue1BHaSRA3J8VTUknQD0mLBHicmD1VPg0qC63/I/1xzD3j90AgBnjPsC0sUWIy9V7+HDCEgCSuGz4dVyBuLT9+GchLmG75ymMyyfz1mPzgUsAgLU/jkLvjo3llu8+FoKRMyRx8e/RHEvnDNW4r+oqjcNfbjOaKc0EiJMgEJWQkPDHOHf1HgBgaM8WBXZ8ADDZvz1qVXMCAKzccgqZWdkat/PHesnBub6+Hn6dNkBuxwcA9jYWmDu5FwAgPjEF6/deLFBHzOtEbDssOXvUvnndAjs+AOjd0Qvtm9cFAGw9eBkxrxVfItd2XnWc4du4BgBgw/7gAskPACzdcgZ3Il8CACYOaA0Dfc12lX06NoKJsSEA4Kc1RwskP4DkqtBPa44CADzdK6NT89oFyqjzDzrk9lOcuXofANCotjPMTYtnLHlZCLv9BBdDJe9l0IfNCyQ/ADB+UFu4u1YEAPy9/UyRtpkVm04AkGwz33/Rv8A2Y2djgZkTegAAEt6mYEvuAZsu4/5MO4XckolLrxYFkh9AEpfauXFZseVkkeKyRCYuv309UGFc5k2RicseZXEJBgB0aF63QPIDAL07NkaH3LhsKcdxAbjNaDsmQFTAiBEjIBaLIRaL37urP6Xp4KnrwushPZorLKOnp4dB3b0BSHZM0p2lupJS0nEmOAIA0NanNqpUtFVYrke7hrA0NwEA7D95rcDyQ2dvIDs7J7evzZS2N/hDybLs7BwcOntDo75qiw/86guvNx4MVlhGLBZj6xHJ8ChbKzO09qqhURuN6+SdtToWdEdpueOX8oar9WzrqVEbsmSH6BkZlt+5bY7IfKcGdPNRWEZPTw99u0q2mYS3KULCpK7klHScD5FsM75NaykdxtGtTQNhmzl0+rrCMrqE+zPtdOB03vv3VzJUU09PDwO7S7YnSVwiNGpDEhfJvqqtTx0VcWkkxGWforickY2L4u8QAAzuIROXM+UzLgC3GW3HBIiohFwMewAAMDc1QqM6VZWWa+nlLrwOuvZAozZCbj1CekYWAKBV45pKyxkZGqCph1vuOo8LnGUKCs1rt5VMf/KTbeNS2EON+qotWuSehUtKSUfY3WdKy50PzXt/zRu4adSGjVXe8IKYuCSl5WLevBVet/IqeOZWHQ425vBrIolLbFwS4hJTilSPNrh8XfI9NDM1QoPayreZFo3yEtLL1zX7HobdfixsM80bqd5mGufehB12u+A2o2u4P9NO6sZF9r0GafheNYmLt6ebsE7+uEj7KqlHVVyK/h3SJtxmtBsTIKISEvFIMoSqmrMjDAyUj8+v5VYxb53cYVeatgFAGBakjHtuO1nZOXjwJEZhPVYWpqjoYKW0DicHa+EsUsSjFxr1VVvUcpXcjB757LVwxkuRiCev8tZx0+wG9pS0vCsyVrmflyJWFnnLXJxsYZo7bK4wRob6cKlki2E9fHBk5STY5iZcK3ec06if2uZe7vfQrYqDym2mhsx3/f5jzbaZezLlaxYyMYG0nazsHEQ+faW03KVrD9Fx+M9w7zQNNTt+hRYD5mPi3AAcPnO91GaaKmncn2kn6WdceFyc8tbR8L3ejcwrLxtfRdxdJe0ojEtuPRrFRcPvkDbhNqPdmABRAQEBARCJRBCJRHj06JHCMq9evcLs2bPh5eUFGxsbmJiYwM3NDR9//DHOnVN8ELZ3716h3q1btxbaj//9738QiUTQ19fH06dP3+Utlbq09Ey8jpec+S9sphQbKzPhvo1nLwufUleWbPnKFRRf+paSvTT+LN+zZ6T1qDOri7Se/HWUB8ZGBnCwlTxfIjpG9XNzEt6mIiklHQBQRcPZbiIe5f1zadVI+ZWdlg3zlunp6aFyBWulZVt5VUfcuV8Qd+4XvDz5I65tn4ElX/dD9dxpbbcdCcGfm05r1E9tkpaeiTcJyQCASo42KsvaWJrBLHebiY7RbJuJjokXXlcqJK6y24Oqdp48f407D58jNS0DaemZePr8DfadCMOYWf+gz6Q/8PxVvNJ1ywPuz7STXFwq2qgs+25xiRdeF/aZVpHpR/52ihYXzfqqLbjNaD8mQKSxwMBA1KxZE99//z3CwsKQkJCA9PR0PH78GBs2bICvry8mT56MnBz5s+sffPABKleuDABYu3atyjaysrKwYcMGAECnTp2EWdvKi6SUvOeXqDNNr5mppExyarqG7eSVNzdTffO7ucyD1vK3I61Hvb4aKayjPJCdYlSd/kuv5FhoOLHAwXN5z5aZNrIjjI0K3pdjbGSAaSM7Ku2fuh5Hv0HfL/7G+AVbkJFZfodpJct+l03V+B6aSL+Hmk1Rrkk7ZnLbTMF2DA0M0Lm1B777vC+2/zEZR/75Etv+mITp4z4QDiSCb0RiyOcrlD4DpDzg/kw7ycVFnW0mt4zs56xxO4V8pmamyvex0nbV2c+Zl+O4ANxmyoPye7cslYmwsDD06NEDGRkZMDQ0xKRJk9CrVy+Ym5sjNDQUP/30EyIjI7Fs2TKYm5vj559/FtbV19fH8OHD8eOPP+Lo0aOIiopSOsXhgQMHEBMjOYs+atQojfoYFRWlcvnz5881qq8o0nLH5AKSZ4AURnrjelp6pmbtyJQv7OZ3I5mD8PztpGVIflenr8a57aRq2FdtYCLzGWSocU9HRqYkjiZqDk2Tuhr+FAfP3kJ33/rwdK+M/UsnYMGqwwjOfdaPt4crvhnfFZ7ulZGekSUkSKqGwIXefoqWH/8OQBJL54o26NaqHgZ2aYylMwfgh7+OYMMBxZM6lAfpGXnfJ0PDEtxmNGhH1TYDAPtXf65wOtmWXu4Y0dcX42evxengu7j3+CUWrT2MuVM+0qiv2oL7M+2Uli4TFzUmPylqXNLl4q+6HdmTPalpiuOi1vZtVH7jAnCbKQ+YAJFGxo0bh4yMDOjr62P//v3o3LmzsMzb2xv9+/dH69atER4ejt9++w3Dhg1D/fp5s26NGTMGP/30E3JycvDvv/9i5syZCtv55x/JXPP29vbo1auXRn3UhqtFpXWgLVteWofSNmR2yPnbMTEyREpahlp9Tc9tR937VbRJaf1TAoCJ32/F9t9Gw8fDFU3ruWDPknEFylwNf4LbkS8x9APJLECqzsympGXitsz48Gt3n+HAmVvYFhiKrb+MxJ8z+qOSo7XGzyzSFsZGed+nTDWuZBV5m9GgHVXbDACFyY+UhZkJVswfgVaDvkNcQjI27ruIGRN6lMtZ+rg/004mxjJxKeTzki2jaVyM5eKvuh3ZZMnURHFc1Nq+M8pvXABuM+UBh8CR2i5fvozgYMkZ5jFjxsglP1K2trZYvXo1ACAnJwfLly+XW169enW0a9cOgOReI0ViYmJw8OBBAIC/vz+MjYv2ZOSyZGGWd3N7shrDDVJyLyWrM4xBvh2Z4QYpqocCJcvcmJ+/HWk96vU1Q2Ed5UFSEYdZJWk4zAoAEpPS8OHklZixZC9uR8rfLPoiNhG/rD2G7pNWwFImhvFvNR8mdebqfazKnfzg65Ed4e7iqHEd2sC8iMMTNX3ukSbtpMhtM5o/X8nKwhQ9O0iep5GSmoHrd8rXvYxS3J9pJ7m4qLPNpKo/BE1pO4V8pimpyvex0nbVGYKXXI7jAnCbKQ+YAJHajh3LO7M8evRopeVatWqFunXrFlhHasyYMQCAe/fuKZwwYf369cjKPcuk6fA3QPL0YFU/ly9f1rhOTZkYG8LeRnqzfbzKsvGJKcLOXtkc/sqoe5M2IH+zZJV8N8xK6ymsr7L15K+jPEjPyEJsnPTGVOUTDgCAtaWp8E/hmRqfiyKZWdlYuf0cWn68EK5d5qDJoF9Qp9cC1O39HX5cE4iMzGzUr1kJAJCYnIZnhUzMoMzBs+EAJA/C69HGo0h1lDUTY0PYWZsDQKGTBsS/TRH+CRd2429+shMsPC8krrLbg6btSMnOvvUitmjxLWvcn2knubgUckP6O8VF5rMp7DOVvTE+fzvSejSLS9G2u7LGbUb7MQEitd28eRMAYGRkBC+vgk8JltWsmeRhWffu3UNGhvxZiT59+sDOzg6A4skQpH9r3LgxGjZsqHE/nZ2dVf5UqlRJ4zqLQjq1ZWTUK2SpuKwsO41lrWqqp7HMT/p0b0B+el9FpFMMG+jroXpV+asE0noSk1LxMlb5051fxCbgbbLk5k7ZA7vyJOKx5N6yalXsCzwxW1YtmSspsrO6FVVichoeRsXi5eu8Z/842lqgehV7AJJ7fIo6ZXJsfLLwuqpT+TxgAPKmaX30LFblNvNAdirrQqZ+zU92ytn7T1THVdqOgb4e3HJn29PU+zINNvdn2kn6GRceF9mprDV7r3WqyU6hXUhcHkvaURyX3JM9msRFw++QNuE2o92YAJHa3rx5AwCws7ODQSE3Qjo5STYMsViMuDj5sxLGxsb4+OOPAQDbtm1DcnLewdvly5dx65ZkBi1VV5nKg+a50x8np2YgTMXQlwuheU9+btZAs4dhetVzFe4pOB9yX2m5jMwsXLn5qMA6QrsyD5Y8H6r8SdSybfg0LNqDO8ta0PVHACSX/BvVrqK0nOyDSS/deFQifenbsRH09CS74d0nrxdSWrlKjnnPbSjKcD1t4e0p+cxTUjNw/a7ybUb2gYrentU0aqNhXRcY5d6EHRSmepsJCX8ss07R7t25J3NwU9Fe+fM1tB33Z9qpeUPJey0sLrLvtZmG71WTuATn7isVxaW5TLvnQ1TFpejfIW3CbUa7MQEijYlEokLLFHbWUzoMLikpCTt27BD+Lr36Y2JigiFDhrxDL8te9zYNhNeb9gUpLJOTk4MtByX3VVlbmsK3aS2N2rA0N4Gft2Sd05fvKn2GwL6T14SzNh+0bVBgeTdfT+jpiXL7eklpe5v3S5bp6YnQzddTo75qiwNn86ao9u/urbCMSCTCwC5NAEiGW50NKf6nkVuaGeMz/7ZCGzsCw4pcV+92eTENf1DysxyWlC4y36lthxQPVc3JycHOw7nbjIUpWqp4orwiFmYmaNVEss2cuxKhdMjHodPXhW2mq2/BbUYdiUmp2HsiFABgamKEBnVcilSPNuD+TDvJvv+N+y4qLJOTk4OtByXb07vH5Y6KuIQJcfmwXcHRG938GsjERfF3CAA275OJi1/Rtj1twG1GuzEBIrVJh629fv1auEdHmZcvJWc9RSIRbG0LDsnx8PAQhslJk560tDRs2bIFAPDRRx/BxsamuLpeJprUd0OL3LMqG/ZexOXrkQXKLN14Qng69viBbWGYb2ayTfuDYOczBXY+U/DT6oMK25ns3x6A5OnO037djuxs+ecvvY5PwrdL9wCQ7GCH9WpZoI6KDlbo36UpAOBE0G3sOR5aoMzuY6E4EXQbADCwm7fKp0Vrs5DbT3Eh7CEAYOiH3vCuX/CgdPIgP9TJHYqwcvt5ZOX7TAd3ayI8lPTrUZ0UtlPR3hIGSobYWZgaY933H8Mp9zP8ZukBJCm4iblvx0awMjcp8HdZvds3wIiekm0p4W0qDp0LV1lem3nVcxXOTm/ZH4SrNwtuM6u2nBKGeozq71dgm9l28BKcfafC2Xcqfv/nkMJ2JgySTMSSlZ2D2Yt2FNhm3sQn4YeV+wBIkqzBPZoXqOPkpdtITVd+tS0pJQ0T5wQgLvfhroM+aKbweVDlBfdn2qlJfTe08MqNy56LuHz9YYEySzeewN3cuEwYpCAu+4Jg6z0Ztt6T8dPqAwrbmTK0AwBJXL76ZZvCuMz7U424dJWcdDquNC4hOC7ExafcxgXgNqPtyu/emEqdh4fk5uqMjAyEhobC21vx2XMAwkQD7u7uMDJSPHvSmDFjcOnSJZw5cwYPHz5EUFAQ4uPjARRt8gNt9OP/+qLbmEVITc9Ev0+X4fMRndG6iTvS0jOx62gI1v13HgBQ06UCJuXuxDTl510bfTo3xq7AEBw6cwN9Ji/FhMHt4ORgjfAH0Vi49giiXkjOCs2d1BM2Voqn7p31SQ8cD7qN2LgkjJ0dgLDbT9CltSTmR87dxLKNJwAADrYWmDnhwyL1VVtMX7IXh1d8AjMTI+xcNBaL/j2Bs6EPYGJkiL4dG2JEL8kB770nr7Bs85kitdG/sxcmD2qDLYev4HxoJF68ToSluQm867tg9EcthHt1NhwIVvr8nhG9mmHRtL44eOYmLlyLxP0nr5CYnAYzEyPUdHFEr7ae6NxSMuFITk4Opi/ZW6SZ5LTJt5/2Qe9PliAtPRNDvliJKR93RIvGkm1m7/EQbNwrOctdvaojxucmMppq1aQWenXwwp7joQg8dxODv1iBMf3boKKDFe48eI4/1h8VzqTOmPAhbBRMd71swzFMmb8e3fwawKdBdbhWcYC5qRESklIRcvMR/t19Xri6VMOlAr4Y1bVoH4gW4f5MO/30v37oOnohUtMz0XeKJC6+TWshLT0TOwOv5otLhyK1IYlLE+wKvIpDZ27go8lLMWFQW1RytEb4/Wj8LhuXyb2UxmX2xB44fjEcsXFJGDNrLULDn6CLb25czt7Eso3HAUjiMmti+Y4LwG1GmzEBIrV17NgRs2bNAgCsWbNGaQJ08eJFhIeHC+soM2jQIHz++edISkpCQEAALl6UHNi4ubmhQ4ei7aS1TYPaVbHmh5EYP+dfvE1Ow4Ll+wqUqelSAVsWTYBlIWf6Vflztj/eJqfh6PlwnL16D2evyo/h1dMT4ctRXTGiT2uldThXtMWm38fj46/+wsvXiVjy7zEs+Vd+Fr+K9lZY/+vYcjszj9SNe9EYPXcjVn0zCFYWppgzoVuBMveevMLAr/5ReGVGXRXtLfGZfzt85l/wQD0zKxvLtpzB/FWHVdZhaWaMgV2bYGDXJkrLxCWm4OtFe7D9aMGzduWNRy1nrPh2OD5dsAFvk9MUnpGuXtUR634ZJzfVrKZ+mzEYb5PTcSIoHBdC7uFCSMFt5rPhnTG0VyuldcQnpmDz/iBs3q98OE+zhjWwdO7HsLUyL3JftQX3Z9pJEpdRGD9nncq4bF088Z3isvQbaVxu4eyVCJy9EiG3XE9PhK9Gd8VIVXFxssXmhRMw9MvVuXE5iiX/HpUrU9HeCht+G1fu4wJwm9FmTIBIbT4+PvD29kZwcDD+/vtv9O3bF506yQ//SUhIwPjx4wEAenp6mDhxotL6LCwsMGjQIPz9999YtWoVYmNjAQAjRoxQ6z6j8qKrryfObZqBlVtO4ej5W4iOiYehoT6qOzuiVwcvjBngJzxvpqhMTYywddFE7Dh8BZsOXMKte8+Q8DYVjnaWaNGoBsb094NPg8JvFm/q4YZzmyV9PXj6Op4+l0x84VLZHt38PDFxUDvY2ZT/AzkAOHz+NloPX4Tx/Vujc8u6qOxojcysLDyMeo09J6/jr50X3ulJ1/tO34SxkQH8GtdEtSr2cLC1QEZGFqJfJeDE5QhsOBCMO5GqZ+0Z9+1mtGnqDt/GNVC/RiU42lnAwcYcGZnZeJOQjPCHL3D80l1sPxqGhHJ+5UdWp1YeOBowDWu2n8bxi+F4/ioBRgb6cHN2wAdtG2FkX1+Yvus2Y2yEf38dh/+OXsX2Q5cRfv8ZEpNS4WBrCZ+GNTCyT2s08VC+zXwzqRfOXY1AyM1HePA0Bm/ik5GYlApTEyNUdLCCV11X9OrYGG186nB/piHuzzTXzc8T5zbPxKotJxF4Ll9cOnph7IA2xRKXbYsnYvvhYGzefwk388Vl7AA/+KhxE39TDzec3zITKzefwoHT1+Ti0t2vASYObgu73Cmk3wfcZrSTSPy+zNFJxSYgIAAjR44EAERGRsLNzU1YFhYWhmbNmiEjIwOGhoaYMmUKevToAQsLC4SGhuKnn37Cw4eSMcjTpk3Dzz//rLKtS5cuoXnzvPH1IpEIkZGRcHV1Lf43lisqKgpVq1YFAEQ8fAJnZ+cSa4uKxs7367LuAikQdeyHsu4CKWFmrF94ISoT71MC/D7h4a/2iYqKQq3qkvtynz59WqLHZ7wCRBpp1KgR9u3bh/79+yMxMRELFy7EwoULC5SbNGkSfvzxx0Lra9asGTw9PXHjxg0AkiFzJZn8EBEREZFu4yxwpLHOnTvj/v37mDlzJho1agQrKysYGxvDxcUF/v7+OHv2LJYuXSo836QwQ4cOFV6/L5MfEBEREZF24hUgKiAnJ28KRWUPPHV0dMT333+P77///p3bk179sbW1xUcfffTO9RERERERKcMrQFRAYmKi8Nra2rpE20pISMDOnTsBAP7+/jA2Ni7R9oiIiIhItzEBogLCwsIAAE5OTrC0tCzRtpYtW4bUVMnsVdLZ44iIiIiISgqHwBEAICYmBg8fPsSpU6ewefNmAEC3bgWfjfKusrKy8OjRI6Snp+PMmTP47rvvAAAffvih8KBVIiIiIqKSwgSIAADLly/Ht99+K/xuY2OD2bNnF3s7UVFRcHd3l/ublZUVFi1aVOxtERERERHlxyFwJNDT00PFihUxaNAgXLx4EdWrF/5As3dRoUIF9OrVCxcuXEDNmjVLtC0iIiIiIoBXgCjXvHnzMG/evBJvx83NjQ8fIyIiIqIywytARERERESkM5gAERERERGRzmACREREREREOoMJEBERERER6QwmQEREREREpDOYABERERERkc5gAkRERERERDqDCRAREREREekMJkBERERERKQzmAAREREREZHOYAJEREREREQ6gwkQERERERHpDCZARERERESkM5gAERERERGRzmACREREREREOoMJEBERERER6QwmQEREREREpDOYABERERERkc5gAkRERERERDqDCRAREREREekMJkBERERERKQzmAAREREREZHOYAJEREREREQ6gwkQERERERHpDCZARERERESkM5gAERERERGRzmACREREREREOoMJEBERERER6QwmQEREREREpDOYABERERERkc5gAkRERERERDqDCRAREREREekMg7LuABFRfhEHF5R1F0gB596/l3UXSIk3h6aVdReIyhWRSFTWXaB8SjMmvAJEREREREQ6gwkQERERERHpDCZARERERESkM5gAERERERGRzmACREREREREOoMJEBERERER6QwmQEREREREpDOYABERERERkc5gAkRERERERDqDCRAREREREekMJkBERERERKQzmAAREREREZHOYAJEREREREQ6gwkQERERERHpDCZARERERESkM5gAERERERGRzmACREREREREOoMJEBERERER6QwmQEREREREpDOYABERERERkc5gAkRERERERDqDCRAREREREekMJkBERERERKQzmAAREREREZHOYAJEREREREQ6gwkQERERERHpDCZARERERESkM5gAERERERGRzmACREREREREOoMJEBERERER6QwmQEREREREpDOYABERERERkc5gAkRERERERDqDCRAREREREekMJkBERERERKQzmAAREREREZHOYAJEREREREQ6gwkQERERERHpDIPirGz+/PnFWZ1gzpw5JVIvUWmJevEGq7aeRuD5W3j2Ig5GRgao7uyAXh0bY3Q/X5iZGBVLOzsDr2LTviCE349G/NsUVLC3QotGNTC6ny+8PaupXDfhbQpCwp8g5NZjhIY/Rkj4Y7yITQQAtGpcE/tWflYsfdQm0S/j8O9/Z3Hq0m08j4mHkaE+XCo7oHvbhhjSsxVM3yEuWdnZiHj4HNfvPMX1u09w485T3H/8Etk5OQCAExtnwdnJTu364hKS8e9/Z3Hs/E08exkHsVgMZyc7dGzlgWEf+cLW2rzIfdVGzo6WGNerKTr71IBzBUtkZGbjYXQcdp+5izX7Q5CanvXObdR2sceoD7zQuoELnCtYwcTIAInJ6bj9+BUOBd3H+sPXkZSaobIOPT0R+rapi37t6qFhzYqwtTRF3NtURDx9jZ2nbmPDkevIzhG/c1+1Cfdn2unpizdYveUUAs/fQpQQF0f07uiF0f39ijEuV7BpXxBu3ZOPy5j+foXGRepNfBJWbT2NA6ev40n0awCAS2V7fNCmAcYPbAM7G4ti6au2YGy0j0gsFhfbnllPTw8ikai4qhNkZ2cXe52ku6KiolC1alUAQMTDJ3B2di7R9gLP3cS4Of8iMSlV4XJ31wrYungi3Ko4FLmNtPRMjJzxD46cu6lwuZ6eCF+P7Y6vRndVWkejXnPx5PkbhctK+4Ah9q3qg87icDIoHF/+sBFvk9MULq9W1RF//TAWLpXti1T/n/8ewZ/rApUu1yQBun7nCT6ZsxYxrxMVLq9gb4UVC0bBs3bVIvVVXbUGLC7R+qU6+1TH6mk9YG1honB5xNPXGPDNdjx+kVDkNib18cbcUW1gaKCvtMyTlwkYMm8nbkW+Uri8op05/v3mI/jUraK0jpCI5xg0ZwdexacUua/qeHNoWonWL8X9meZK4rgovyPnbmLcN+tUxKUiti2eCDfnd4vLiOlrVMZl+tju+GpMN5X1hNx6DP8vVwkJaX6VHK2x8bdx8KrnWuS+ahPGRn1RUVFwryb5P/b06dMSPT4r9gSoJOTknjGlwgUEBGDkyJEAgMjISLi5uZVth7RQaSZANyOi0HXMIqSkZcDCzBhTh3dC6ya1kJaegV1HQ/Dv7gsAAHe3ijge8BUszIyL1M64b9Zhx5ErAADfJu4YP6gtnBytEX4/GosCAhEZFQsAWDxzMIb1bqmwjoa95uJp7gFDBTtLeNVzFXam71sCdPtBNAZ9+idS0zJgbmqMcYPbo3mjmkjLyMSBk2HYdiAIAFDdpQJ2Lp8Kc1PN4/LHuiNY+q8kATI2MkDdGlXwJiFJOKOmbgL04lUC+kxchNi4tzDQ18PIfm3Qtnk9AMCpoHCs3XEaWdk5cLC1xK4Vn8PJ0VrjvqqrNBIgj2qOOLxoKMxNjPA2JR2Ltwbh7PUnMDEyQJ82dTGieyMAwN0nr9Hh03VITsvUuI3evnWwdlYvAEB6Rhb+3h+K06GP8DohFdUq22D0h15o4SHZRzx//RbNx/6NxBT576SJkQEOL/RHw5pOAICTIY+w9kAoHr9MgJ2lKXr51sbHXRpAX18PIRHP0e1/G5GRWXIn80ojAeL+rGhKOgG6ERGFrqMX5sVlRGf4NnFHWnomdgVexbrcuNRyq4jj66YVOS5jZwfkxaVpLUwY1BZODtYIfxCNhWuPCHFZMmuI0rhEx8Sj3cc/I+aNZH/2iX97dGntAUCSKCzfeAJZ2TmoYGeJk+u/RuUKNkXqq7ZgbDRTmglQsQ6BY6JCJG/mol1IScuAgb4edvwxCT4N8i5B+3nXRvWqjpj35x7ce/QSyzedwLRCzs4ocj7knrDj6+rrgfW/jIW+vuRkRON6rujm54l2w35B1Is4zFu6B706NIK1pVmBesb094NrZXs0ruciHJjb+UwpytvWej8s243U3Lj88/M4eNV3E5a18HKHWxUH/LJ6Px4+icHa7acxeVhnjdvwqueK+VP7wbN2VdSuUQkG+vr4+ufNQgKkrkX/HERs3FsAwO+zhqJbm4bCMu8G1eFRuyo+m/8vYuPeYknAIfz41SCN+6pNfpjQAeYmRsjMykbfWdsQfDtaWHb22hM8jI7D/DHtUNvFHp/08cavmy5o3MZXQ/IOAIZ99x8CLz8Ufg+JeI6dp25j3eze6Nm6NirZW2Jo14ZYvitYro4xPRoLyc+GI9cxZdEhueWnQh8h+HY0lv2vOxrXqoQxPRoXqKO84f5MO81cuFOIy84/J8GnQXVhmZ93bVR3qYC5f+xGxKOXWL7xBKaNLUJcrsrHZcOv4/LiUl8Sl7Yf/4yoF3GY++dupXH5bvk+xLyR7M/++m4EendsLCxr6VUTXnVdMHLGP4h58xY/rNyPpXOGatxXbcLYaC9OgkBUQkLCH+Pc1XsAgKE9W8gdLEhN9m+PWtUkB1Ert5xCZpbmZ4j/WH8MAKCvr4dfpw0QdnxS9jYWmDtZcrY7PjEF6/deVFjPlKEd0LN9I43uSymPrt95gkvXHgAA+nVrJpf8SI3q3wY1XCoCANbtOlOkuPh618GgHi1Qv5YzDPSVD7NSJfbNW+w9FpJbX2255EeqW5uG8PWuDQDYHXgVsbn/wMojr1pO8G0oGVqx4ch1ueRHaunOy7jzWHI2c+JHTWGgr9m/MUszI9Sr5ggACLv3Qi75kfXLxvPCa0VD3AZ3lJwZTUrNwKxVxxXWsenoDQTdigIAfNrPB6UwEqrEcH+mnUJuycSlVwu5A2ypyf7tUTs3Liu2nCxSXJbIxOW3rwcqjMu8KTJx2VMwLjGvE7HtsOQkQIfmdeUOsKV6d2yMDs3rAgC2HLysdNhvecDYaDcmQEQl5OCp68LrIT2aKyyjp6eHQd29AUh2TNKdpbqSUtJxJjgCANDWpzaqVLRVWK5Hu4awNJfcT7H/5DWN2njfHDufN0a6b1dvhWX09PTQu3MTAEDC21RcDrtfKn3L7/jFW8KkCX26KO4rAHzUWbIsOycHxy/eKpW+lYQPWrgLrzcG3lBYRiwGth6XxNDW0hStG7ho1IbsPT+PXsQrLRcZnbfM2FA+gTUxMhCSqODb0QWGx8k6dkWSYFW0sxCG1ZVH3J9ppwOn896/f48WCsvo6elhYHcfANK4RGjUhiQudwEAbX3qqIhLIyEu+xTE5dCZG8jOluzPlH2HAGBwj2YAgOzsHBw6o3g/UB4wNtqNCRBRCbkYJrnKYG5qhEZ1lB/4tPTKO+gLyr0yoa6QW4+QniGZDatV45pKyxkZGqCph1vuOo+LdJbpfXHlRiQAwMzECPVrKR9f7NOgRt46NyNLvF+KXLmRd3XCp2ENpeVkl129UTZ9LQ7SBCEpNQNh914oLXf+xlPhdfP6mo0Rf5OYijeJkpuR3ZxslJarVjlv2f1n8jfT28hMzvAqPllle6/i8iY/aOVZfhMg7s+0k7pxkf08g8IUX/VURpO4eHu6Cevkj4u0r5J63KGM7DJNv0PahLHRbqWeAN2/fx9z5sxBp06d4OnpiZo1a+L+ffmzqzdv3sTBgwdx+vTp0u4eUbGJePQSAFDN2REGKmaaquVWMW+dyJdFagOQzCSjintuO1nZOXjwJEajdt4nD55IPjOXKg4qh6ZVd6kgvH5YRp+XNE6W5iZwtLNSWq6CvRUscs/uSd9feVSrqmTGvcjoOJVTR0c8zbuPqpaL5rP0rTsUBgBo5O6Ejk0LDksBgC9z7xPKys7B+sPX5ZalpOVd8bEq5KZlK/O85XVcij7LU1nj/kw7ST/jwuPilLfOI+UnFxS5G5lXXja+iri7StpRFJeI3HqsLExR0UH5/szJwVq4WqHpd0ibMDbardQSoJycHHz11VeoW7cuvv/+exw/fhy3bt1CZGQkMjLkhw88ffoUH374ITp16oRnz56VVhd1xqtXrzB79mx4eXnBxsYGJiYmcHNzw8cff4xz584pXGfdunUQiUQQiUQ4duxYoW188sknEIlEMDIywuvXim/6vnv3Lj799FPUr18f1tbWMDU1RfXq1TFy5EiEhIS803ssa2npmXgdnwQAhc6UYmNlBnNTyTMAnr2M06gd2fKVKyi+9C0le2n82ct4jdp5X6RnZCIuQXLG3slB9Wxp1pZmwrMZnsfEl3TXFHqR266To02hZSvllnnxKr7E+lOSjA314WAjuTE3Olb1fUwJSenC83mqOFpq3Nbvmy/iZMgjAMD6OR9h/ph26Ni0OrxqOeEjvzrY98tg9Patg6zsHExfcUwu4QKAxJQMPH8t6WPTOpVhaKD8X2lLz7wrVM4VlB9YaDPuz7STXFwq2qgs+25xiRdeFxb/KjL9yN+O9Hd1Zg+TxlfTvmoLxkb7lVoCNH78eCxcuBDZ2dmoXLky+vXrp7Rst27dUL16dWRnZ2PHjh2l1UWdEBgYiJo1a+L7779HWFgYEhISkJ6ejsePH2PDhg3w9fXF5MmTC8zo16dPH5iamgIANm7cqLKNrKwsbN++HQDQtWtX2NsXPEO7YMECeHh44M8//0R4eDgSExORlpaGyMhIBAQEoGnTppg7d24xvevSl5SS92wZczWmtTTLnWY5OTVdw3byypubqX6QmrnMg9Y0bed9kSzzeZmpMbW19EGoKWll83lJ42RmWvhD8oS+FvLgTm1lYSb7/Sx8auuU3OmvLYrwAMHktEz0n70Nny4+hOjYt5jSzwfbv+uPE38Mxz8ze6F1AxfsPXcXXb/YgDX7QxXWceiiZOSCg40ZJvf1UVimef0q6OydNzzRQo04aiPuz7STXFzU2J9J4yL7OWvcTiHxl92v5o+LtF11pnqWJgTlNbaMjfYrlQTo1KlTWLNmDQBg5syZePToEbZt26Zynf79+0MsFuPkyZOl0UWdEBYWhh49eiAxMRGGhoaYOnUqTp48icuXL2PVqlWoVk0yq8+yZcswY8YMuXUtLS3Rs2dPAMCuXbuQlqb44ZEAcOTIEcTGSmZp8vf3L7B8zpw5mDNnDrKystCyZUv8/fffuHjxIq5cuYKNGzeiRYsWEIvFmD9/Pv7888/ievulKi0j7yn1RioufQtlDCUz0qela/ZME9ny0jqUtmGUt1zTdt4X6Rl579vQUI24GBUtLsVF2l9Dg8KfWFDU75C2MJH5/maocU+H9Jk6JsZFe5qDV61K6NumrtL7gNp6uWFwRw9YKjkQX7wtCInJkgOA2cP98P249qhe2RYG+nqoYGuOMT28sHV+f4hEImG8fVH7Wta4P9NOaekycSnk85Ito+nnlS4Xf9XtGMvEJTXfM7rSpPszDfa9qeU0toyN9iuVvfHKlSsBAN27d8d3332n1jo+PpIzarduld8ZjbTNuHHjkJGRAX19fezfvx+dO+c928Tb2xv9+/dH69atER4ejt9++w3Dhg1D/fr1hTL+/v7YunUrEhMTsX//fqVX8TZt2gQAsLCwEJImqeDgYHz//fcAgNmzZ2PBggVyy5s0aYJBgwZh+PDh2LBhA2bNmoWPP/4YNjY2ar/PqKgolcufP3+udl1FZWKk6cGcZCdmYmyoWTsy5aV1KG1DZkepaTvvC2OjvPedqcZDKaWfWVl9XsZGhkhNy0BmlurYAkX/DmmLtExND7IlZWQPNNTVs3VtrJr2IUyMDHDzYQx+XH8OF248RVJqBqo4WqJPm7r4cnBLjO7RGC09q6L3jK2IiZOf7OBpTCJGfL8bAbN6w8rcGJ/08cYnfQrO1Df375P4ckhLGBroI0nFbHHajPsz7SSbUBf2ecmW0fTzMpaLv+p2ZA/ITU3k2zExMkRKWoZG+17Tchpbxkb7lcoVoIsXL0IkEmH06NFqryN9+uuLF5rdEEaKXb58GcHBkjnex4wZI5f8SNna2mL16tUAJPdsLV++XG657HA2ZcPgkpOTsWfPHgDyw+akfv75Z+Tk5KBJkyaYP3++wjr09PTw559/wtjYGG/fvtV4GGTVqlVV/kiT65JkYZY3S1SyGpe0U3IvJatzqVy+HZlL2oUcXCXL3LitaTvvC9khAilqXL5Pzf3MzEzK5vOSxkmdYW1CX8vtMCvZ72fh/1jNcv+BJ6VpllQ42phh2f+6w8TIALcfvUKXzzfg4MV7iE9KQ1Z2Dh6/SMCirUEYPG8ncnLEqOvmiJ8/6aiwrpMhj+A3aS3+PXwNrxNS5JZdvv0M/Wdvx9/7QmGZ+72LT1J+5VybcX+mneTiosb+TBoXdYY5KW2nkPjL7lfzx0XarjrDvJJz93nlNbaMjfYrlQQoJkYy24R0iJU6DHIv5WVmlu9LbNpCduICVYloq1atULdu3QLrAIChoSEGDBgAADh48CDi4+MLrL9nzx4kJ0vOlOYf/paZmYlDhyRPS+/Xrx9EKp4KaGNjA09PTwCSBLq8MTE2hL2NBQAgupAb6OMTU4QdirI5/JWRvWExOkb1DYmyNyxWKeSmzPeVsZEhbK3NAQAvYhNUlk14myLM9lVJjRtDS4KTo2SiBnUmNnieW0adCRO0UXpmNmLjJUlEZQfVExtYWxgL99M8e6XZg1/7tKkrrLtwy0WkKBnGcSbsMU6HPQYA9GhZC9YWiv/ZP36RgM8WH0bNgX+izpClaDp6NVz6LEKXzzfg2JWH8KieN5vg3SexGvVVW3B/pp3k4lLIRBDvFBeZz7ew+MvelJ+/HWk9hdUhqSdOYR3lBWOj/UolAZJeBUhJSSmkZJ4nT54AkFyVoHd386bkwYFGRkbw8vJSWbZZM8mDru7du1dghj5pUpORkaHwyoz0ylDFihXRoUMHuWXh4eHCd2DGjBnCrHLKfq5cuQJA86uAT58+Vflz+fJljeorKumUlJFRr5ClYtiI7NSvtaqpnsYyP+kTpAHg3mPVU1Ley23HQF8P1as6atTO+6SGi+QzfvIsFlnZyuMiO/W17JTYpalG7lTAb5PT8OqN8qdux7xORFKy5OqC9P2VR9LZ1qpVtoW+nvITJNLpsgEg4oniWSaVqS0zbfa1+6q3mWu5zyLS19dDzSp2hdb98k0yHjyLw1uZqxfN61cRXl+9W/LDb0sK92faSfoZFx4X2emSnZSWU6RONdlpmguJy2NJO4riUrtaJQBAYlIqXsYq35+9iE3A29z9mabfIW3C2Gi3UkmApFd+QkMVz6ajyP79+wEA9erVK5E+6Zo3byQP8rOzsxOurinj5CTZoMRiMeLi5M/CtWzZEm5ubgAKDoOLjY1FYGAgAGDQoEHQz/eMFemVQE1pkjgDkuGTqn4qVapUpH5oqnkjyfNFklMzEHbnqdJyF0LznpberIHiZ5Io41XPVbh58nzIfaXlMjKzcOXmowLr6KImHpL9UUpaBm5FKL9f7PL1vIe8SdcpbbLtXlbx0DnZZY1zHxBZHgXdksTDwtQIjdyVHwjIPlD0Urjqe/7yy8rOm+HSQF/1v0ADmemtZdfTRL92kv9hKWmZOBSkfBvVdtyfaafmuQ9BLiwusp9ns4YlF5fgG48KrJPX17x2z4fcgzKyyzT9DmkTxka7lUoC1LlzZ4jFYqxevbrA9MqKXL16FevXr4dIJELXrl1LoYe6Q9WwMymxWPkDCEUiEYYMGQIAOHPmjNxzmrZt24as3JvwFM3+li1ztv3XX3/FjRs31Pr5559/1H5/2qR7mwbC6037ghSWycnJwZaDknuzrC1N4du0lkZtWJqbwM9bss7py3eVzsu/7+Q14azNB20bKCyjKzq29hBe7zwcrLBMTk4OdgdeBSB5MFyzRsqfsF2SOrSsD73cKyG7jijuKwD8FyhZpqcnQoeW9ZWW03YHLuT9c/Xv7KmwjEgEDOwgiWH82zScvfZEozYev8gb+tjCw1lFSaBlbqKVkyPGk5eqh0wq0su3NjxrSM6S7jgVLswaVx5xf6adZN//xn2Kh4vn5ORg60HJyId3j8sdFXEJE+LyYbuGBZZ382sg7M+UfYcAYPO+SwAk+7NufuU3voyNdiuVBGjy5MkwNTXFjRs3MHbsWJX39ezcuRNdu3ZFRkYGrKysMG7cuNLo4nvPzk4yfOP169dCkqLMy5eSy6gikUjhEMShQ4cCkGy4mzdvFv4uvSLk7u4Ob++CsyHJPg8oMzMTHh4eav1ocu+YNmlS3w0tGknOAG3YexGXr0cWKLN04wnhCczjB7aFYb7ZrzbtD4KdzxTY+UzBT6sPKmxnsn97AJIz1NN+3Y7sfGeqX8cn4dulkokprC1NMaxXy3d7Y+VcwzouaOopOXO149AlhN56VKDMP9tP48ETyXYwrI9vgbjsOnwZtTr8D7U6/A9/rDtSYn11tLNCjw6NAQBng+/i8OlrBcocOn0NZ4PvAgB6dWwCR7vy+bBNAAiJeI4LNyRnSod2aQDvupULlJnc1wd1XB0AACv3XClwZWZwJw/EHf4acYe/xtdDWxVYP/DyA+TkSE7yfDGoJSrZWyjsy/BuDdG4luRq8ZU70Yh7W3ACA2cVD2FtUrsS/pjaDQDwJjEVC9aeUVq2POD+TDs1qe+GFl65cdlzEZevPyxQZunGE7ibG5cJgxTEZV8QbL0nw9Z7Mn5afUBhO1OGSoa0Z2Xn4KtftimMy7w/VcelooMV+neVHBscD7qNPccLjgrafSwEx4NuAwAGdvNBRYfyuz9jbLRbqVw3rlKlCv744w+MHTsWAQEBCAwMRI8ePYTla9asQUpKCo4dO4aHDx9CLBZDJBJh9erVsLZW/bR2Uo+Hh+SMaUZGBkJDQxUmKFLSe2Tc3d1hZFRwRqm6devCy8sLoaGh2LRpE7788ks8evRImKxA0dUfAKhfvz6MjIyQkZGBwMDAAs8aeh/9+L++6DZmEVLTM9Hv02X4fERntG7ijrT0TOw6GoJ1/50HANR0qYBJuf/4NeXnXRt9OjfGrsAQHDpzA30mL8WEwe3g5GCN8AfRWLj2CKJeSM4KzZ3UEzZWZgrruRERhRtKhoTFvE7Epv3yZ4V6tvfSeMYabTF7Ui8M+mwp0tIzMerr1Rg/pAOaN6qJtPRMHDgZiq0HJO+1mrMjRvVvU6Q2klPTcSRfwvI4Ou8m+CNnrsHWylz4vU7NKqhXswry+2JUN5wNvos38Un44vsNuBHxFO2aS4ZVnQwKx9rtpwEAdjYW+HxUtyL1VZtMX3kMh38fCjMTQ+z8fgAWbQ3C2WtPYGJkgL5t62JE90YAgHtRr7Fsp/KrYsrci3qDjUdv4OMuDVDF0RKnl43Ayv+u4OLNKLxNzYBz7jTY/dtLrqRlZedgfsBphXVtnd8fSakZ2HEyHDcfxiA5PROV7S3Qtbk7hnTygKGBPtIysjD2532ITdBsKK824v5MO/30v37oOnohUtMz0XeKJC6+TWshLT0TOwOv5otLh0JqU0wSlybYFXgVh87cwEeTl2LCoLao5GiN8PvR+F02LpN7KY3L7Ik9cPxiOGLjkjBm1lqEhj9BF1/J8cmRszexbONxAICDrQVmTfywSH3VJoyN9iq1gbOjR4+GSCTCp59+imfPnmHVqlXCcKzFixcDyBt6ZWxsjJUrV6J///6l1b33XseOHTFr1iwAkoRTWQJ08eJFhIeHC+so4+/vj9DQUISGhuL27dv477//hPhJh8jlZ2Zmhg4dOuDQoUM4deoULl++XCpTUpelBrWrYs0PIzF+zr94m5yGBcv3FShT06UCtiyaAEtzEwU1qOfP2f54m5yGo+fDcfbqPZy9Kj+GV09PhC9HdcWIPq2V1nHg1HX88vchhcvuPY7B5Pny93y1buxebg8Y6rk7Y/Hsj/HlT5uQlJyGhWsKno2u5uyI1T+MkZtmVBNxCcmY/utWpct/XrVf7vfJwzorTIAqVbDFqu9G4ZM5a/HqzVv8teUk/toi/4BoRztLLJ8/stzOACfrxoMYjP5xD1ZN6wErc2PMGVkwAb0X9RoDv9mBJDWmB1fky6WBMDc2RJ+2deFoY45vFLQBAEmpGfh8yWGcv654/L5IBPjUqwKfegXjBgDPXiXik98P4kzubHLlHfdn2kkSl1EYP2edyrhsXTzxneKy9BtpXG7h7JUInL0SIbdcT0+Er0Z3xUgVcXF2ssXmhRMw9MvVePk6EUv+PYol/x6VK1PR3gobfhtX7mcZAxgbbVaqdw6OGjUKnTt3xuLFi7F3717cvy9/w1aVKlXQs2dPfPXVV8KN9lQ8fHx84O3tjeDgYPz999/o27cvOnXqJFcmISEB48ePByB5Fs/EiROV1jd48GBMmzYNOTk52LhxI3bv3i204+7urnS9WbNm4fDhwxCLxRg0aBCOHj2KGjVqKCybnZ2NrVu3ws/PT3guVHnU1dcT5zbNwMotp3D0/C1Ex8TD0FAf1Z0d0auDF8YM8IOZybs9u8XUxAhbF03EjsNXsOnAJdy69wwJb1PhaGeJFo1qYEx/P/g0KJ9DCUtK+5b1se+v/2HdzrM4fek2XsQmwNBAHy5VHNDNrwGG9m4N03eMS3FpWNcV+/7+Ev/uOotj52/iWe7ZPGcnO3RoVR/D+/gJ03u/Dw5feoDWE//B+N5N0dmnOio7WCIzMwcPn8dhz5k7+GtfCFKL8ABUqYzMbIz+aS/WHgzDkE4eaFq3MirZW8LYUB9vUzJwP+oNToU+wrpD1xAdq3ya7W/+OomuzWuiWb0qcLK3gLW5Cd68TcW9p6+x/8I9bDxyHclpyod8l0fcn2mnbn6eOLd5JlZtOYnAc/ni0tELYwe0KZa4bFs8EdsPB2Pz/ku4mS8uYwf4wUeNG+Oberjh/JaZWLn5FA6cvoanzyWTNLlUtkd3vwaYOLgt7GwUD00tjxgb7SQSq7rjvYQlJiYiJiYG2dnZsLe3h4ODQ1l15b0REBCAkSNHAgAiIyPlEsmwsDA0a9YMGRkZMDQ0xJQpU9CjRw9YWFggNDQUP/30Ex4+lIxRnTZtGn7++WeVbXXs2BHHjx+HjY2N8EygJUuW4NNPP1W53rx58/Dtt98CACwsLDB69Gh07twZlSpVQnp6ujCcbseOHYiOjsaNGzeEIXzFISoqClWrSm5ujnj4pFwnV++r2LdFO7NPJavWgMVl3QVS4s2haWXdBVJCncmHiEhyfOZeTXJ89vTp0xI9PivTuSOtrKxgZVW+b6IqTxo1aoR9+/ahf//+SExMxMKFC7Fw4cIC5SZNmoQff/yx0Pr8/f1x/PhxIfnR19fHwIEDC11v3rx5sLGxwfTp05GUlIQlS5ZgyZIlCssaGRnBxKTol4WJiIiIiGSVyixwpD06d+6M+/fvY+bMmWjUqBGsrKxgbGwMFxcX+Pv74+zZs1i6dCn09Ar/avTt21cuOenQoQMqVlTvwVhTp07FgwcP8M0336B58+ZwcHCAgYEBzM3NUatWLfTt2xcrV67Es2fPULNm2UxBTERERETvnzIZAnf16lUcO3YMN2/elHtAp4eHBzp27IgmTZqUdpdIh3AInPbjEDjtxCFw2otD4LQXh8ARqee9HQJ35coVTJo0CVeuXFFaZubMmWjatCmWLVuGpk2blmLviIiIiIjofVdqQ+C2bduGVq1a4cqVKxCLxRCLxTA0NETFihVRoUIFGBoaCn8PDg5Gq1atsH379tLqHhERERER6YBSSYDu3LmD4cOHIzMzE/r6+pg4cSKCg4ORnJyM6OhoPH/+HMnJybhy5QomTpwIAwMDZGZmYtiwYbhz505pdJGIiIiIiHRAqSRAP//8M9LT02FiYoLAwEAsW7YMTZo0gb6+vlBGX18fjRs3xrJly3D06FGYmJggIyMDv/zyS2l0kYiIiIiIdECpJEDHjx+HSCTC1KlT0bZt20LLt2nTBlOnToVYLMaxY8dKvoNERERERKQTSiUBevXqFQCge/fuaq/zwQcfyK1LRERERET0rkolAXJ0dAQAjR5oaWxsDABwcHAokT4REREREZHuKZUEqFWrVgCA4OBgtde5fPkyAKB169Yl0iciIiIiItI9pZIAffHFF9DX18cPP/yg1pC2mJgY/PjjjzA0NMTnn39eCj0kIiIiIiJdUCoJkLe3N1atWoWYmBg0a9YMu3fvRk5OToFyOTk52LNnD1q0aIFXr15hxYoV8PHxKY0uEhERERGRDjAozspGjRqlcnm9evVw7do19O3bF7a2tvDy8kKFChUgEonw8uVLhIWF4c2bNwCAhg0b4ty5czh//jzWrFlTnN0kIiIiIiIdVawJUEBAAEQikcoyIpEIYrEYb968wYkTJ+SWicViocy1a9dw7do1AGACRERERERExaJYEyAXF5dCEyAiIiIiIqKyUqwJ0KNHj4qzOiIiIiIiomJVKpMgEBERERERaQMmQEREREREpDOYABERERERkc4o1nuANJGdnY24uDikpqYKs78p4+LiUkq9IiIiIiKi91mpJkCxsbH4888/sXv3boSHhyt8GGp+IpEIWVlZpdA7IiIiIiJ635VaAnThwgX06dMHr169KvSKDxERERERUUkolQTo9evX6NWrF16/fg0LCwuMGTMGNjY2mDdvHkQiEf7++2/ExcXhypUr2LNnD9LS0tCqVSuMHj26NLpHREREREQ6olQSoKVLl+L169cwNjbGxYsXUb9+fdy6dQvz5s0DAIwcOVIo++LFCwwZMgSnT59GixYt8PPPP5dGF4mIiIiISAeUyixwhw4dgkgkwqhRo1C/fn2VZZ2cnHDgwAHUqFEDv/32G06cOFEaXSQiIiIiIh1QKgnQ/fv3AQAdO3YU/iYSiYTX2dnZcuVNTU3x+eefQywWY+XKlaXRRSIiIiIi0gGlkgAlJiYCAFxdXYW/mZiYCK/fvn1bYJ2mTZsCAC5dulTCvSMiIiIiIl1RKgmQhYUFAMhNZ21nZye8fvToUYF10tLSAAAxMTEl2zkiIiIiItIZpZIA1axZEwDw5MkT4W82NjZwcnICAJw8ebLAOhcuXAAAmJubl0IPiYiIiIhIF5RKAtSsWTMAQHBwsNzfu3btCrFYjF9++QURERHC3y9fvoxffvkFIpEI3t7epdFFIiIiIiLSAaWSAHXp0gVisRi7du2S+/sXX3wBAwMDxMTEwMPDA97e3qhfvz5atWqFuLg4AMBnn31WGl0kIiIiIiIdUGoJ0LBhw9C8eXNERkYKf/fw8MCKFSugr6+PrKwsXL16Fbdv3xZmhZs3bx66du1aGl0kIiIiIiIdUCoPQjU0NERAQIDCZaNHj0br1q0REBCAW7duISsrC+7u7vj444+FmeCIiIiIiIiKQ6kkQIWpXbs2fvzxx7LuBhERERERvedKZQgcERERERGRNmACREREREREOoMJEBERERER6YxivQdIX1+/OKsDAIhEImRlZRV7vUREREREpHuKNQESi8XFWR0REREREVGxKtYEaO7cucVZHRERERERUbFiAkRERERERDpDK54DRFRW0jKzkZKRXdbdoHzsLYzKugukQNzhr8u6C6SEbYdvy7oLpMSLw7PLugukgLFh8d+3TuUHZ4EjIiIiIiKdwQSIiIiIiOj/7N13VBRXGwbwh96rNJWqgg0VULEDlti7sTfsGktMYmKKiRo1MYkak09ji70bO/besSKKgIoKSEdEpHe+PxaWxS3sYmFxn985HFem3MvcnTvzztxCKoMBEBERERERqQwGQEREREREpDIYABERERERkcpgAERERERERCqDARAREREREakMBkBERERERKQyGAAREREREZHKYABEREREREQqQ7MyEn327Bn8/f0RHx+PzMxMTJkyBRYWFpWRFSIiIiIiUiEfNAC6e/cuZs6ciStXrpT5/YABA8oEQCtXrsT8+fNhYmKCkJAQaGlpfchsEhERERHRR+qDNYE7evQoWrdujStXrqCoqEj4I8no0aORlZWFZ8+e4ciRIx8qi0RERERE9JH7IAFQfHw8hg4dipycHDRo0ADHjx9HWlqa1PUNDQ3Rt29fAMDx48c/RBaJiIiIiEgFfJAA6M8//0R6ejocHBxw+fJldOnSBQYGBjK38fHxQVFREe7cufMhskhERERERCrggwRAJ0+ehJqaGr766iuYmprKtU3dunUBABEREe8vY0REREREpFI+SAAUHh4OAPD09JR7GyMjIwBAenr6e8kTERERERGpng8SAOXl5QGAQqO5paSkAEC5TeWIiIiIiIjk9UECIBsbGwClb4Lk4e/vDwCwtbV9L3kiIiIiIiLV80ECoDZt2gAADhw4INf6mZmZWL16NdTU1ODl5fU+s0ZERERERCrkgwRAo0ePRlFREXbu3IlTp07JXDc9PR2DBg3C8+fPAQDjxo37EFkkIiIiIiIV8EECoE6dOqFv374oLCxE79698fXXX+PmzZvC5cnJybhx4wYWLFiAunXr4vjx41BTU8OoUaPg7u7+IbJIREREREQqQPNDJbRt2zb07NkTFy5cwLJly7Bs2TKoqakBALy9vYXrFRUVAQA6duyI1atXf6jsERERERGRCvggb4AAQF9fH2fOnMEff/wBGxsbFBUVSfwxNzfHL7/8gpMnT0JHR+dDZY+IiIiIiFTAB3sDBADq6ur46quv8Pnnn+PmzZu4ffs2EhMTUVBQgGrVqsHd3R1t27Zl4ENERERERO/FBw2AhIlqaqJ169Zo3bp1ZSRPREREREQq6oM1gSMiIiIiIqpsDICIiIiIiEhlfJAmcFu2bHmr7UeNGvWOckJERERERKrsgwRAvr6+wiGvFVUyHxAREREREdHb+mCDIJTM70NERERERFRZPkgAFB4eXu46GRkZePToEbZv3479+/ejdevWWLt2LQwMDD5ADomIiIiISBV8kADIwcFBrvUaNGiAfv36YefOnRg5ciSmT5+OM2fOvOfcERERERGRqlDKUeCGDh2KUaNG4cKFC1izZk1lZ4eIiIiIiD4SShkAAcCgQYNQVFSETZs2VXZWiIiIiIjoI6G0AZCNjQ0A4NGjR5WcEyIiIiIi+lgobQD0/PlzAEBeXl4l54SIiIiIiD4WShkA5eXl4ffffwcA1KlTp5JzQ0REREREH4sPMgpcydscWQoLC/Hq1Svcvn0bK1aswIMHD6CmpoYhQ4Z8gBwSvV8x8a+wYe8lnPUPRkxCCrS1NOFoa4Fe7d0wun9b6Olqv5N0Dp0JwJ5jNxD6NA6v0zJhaW4Ezya1MbpfWzR1dZS5bctP5yM6/lW5adjamOH63rnvJL+VLTo+GWt3X8Spa8GIiX8FbW1NONlaoG9HD4z9tB3031G57D91BzuOXEfIk1ikpGXCqpoxWjapjXGftkPzRk4yt32dlomAkOcICInE3ZBIBIREIiEpFQDQxqMODq/6/J3kUdlExSdj7a4LOHU1GNHFZVPL1hJ9O7lj3ECvd1Y2+07dxg6/6wgOKy2bVm61MX6gV7llUyI5JR1rdl/E0Yv38Tz2JQDAvkY19PBujEmDvWFuavhO8qosbK2MMbFfC3Ru4QxbKxPk5hXgWWwyDl4IxvrDt5CVk//WaTSsZYXxfTzR1s0R1asZoaCwCDEvXuP0jTCsO3gT0YmpMrc3NtCBR72aaFq3Jtzr1YBH3ZqobmEEALgSGIFeX21+6zwqm+j4ZPz73yWcvRaC6IRX0Cm+zvTu6A7f/m3f2Tlz8HQAdh29gZCnscXXGWO0bFILYwa0k3mdycnNx1n/YNwNeY7A0OeISXiF5JQMpGdmw8hAF7XtrdCueV2M7NMaNaxM30lelQXrM+WjVvQBZijV0NBQeJuioiK0atUK586dg46OznvIFamq6Oho2NnZAQDuPwpHjZq27zW9s9eCMf3nrUhNz5a4vLa9Fbb8MREONS0qnEZ2Th4m/7gJZ64FS1yurq6GL8d2xUzfLlL3oUwBkJ6W4nWGok5deYBJc7cgNT1L4vI6DlbY/ecUOL5luYz9fgNOXnkgcbm6uhpmj++OWeO6St2He9+5eB6XLHHZhw6A1NXVPkg6J688wMQfN0stG2cHa+xZPgWOtm9XNr7frpdZNt9O6I6vx3eTuZ+A4EgMn7UG8UmSb8irW5pg+5KJcG8g33QQFWXWcf573X+Jzi2csfb7/jAx1JW4/PHzJAz6fjsi41IqnMa3o33w9Qgvqd+31PRsfPb7IRy9+lDqPgK3zYBDdTOJyz50ABR/Ys57T+P01WBMnb9Ven1mb4XtSye99XVmwpyNOH1V+nVm1rhu+HKM5OtMePQLtBq0sNx09PW0sXjWQAzq5lnhvMpD5wNcZwDWZ4qIjo6Gs5Pg/iwqKgq2tu/v/uyDvAFSNMYyNzfHxIkT8eOPPzL4oSotJCwGk3/ajKzsXBjo6WDayE5o7VEH2Tl5OHTmLnb4+ePp80SM/mYtjq77Cgb6Ffu+z1q8Sxj8tPZwxriBXrC2MMHDp7H439YziIxJwpJ/j8OqmjGG9Wolc19d2rni6wk9pC7X1vwwF4336cHjaIz7YSMys3NhoK+DmaM/QbumLsjKycWBUwHYcugankQmYsiXq3Fm49cwrGC5fL5oh/CC1LapMyYN9oGNpQlCn8Tiz82nEB6dhF/XHoW1hTFG9mktcR+itaeVuRHcGzhIvch9DIIeR2PsdxuQmZ0LQ30dzPTtjHZNnZGdk4f9p+5g88FrCItMwOAvVuHs5m8qXDbTF2wXHsd2zVwweYgPbCxMEPI0Fss2nkR4dBJ+WXMU1hYmGNVXctnEJqZg6JerkZicBk0NdXw2vAO6tHUFILjp+Wf7OcS9eI0hX6zG+a2zq/xTbdda1tjw46cw0NNGWmYOlu+8gsuBEdDV1kT/9q7w7dkULvYW2L1oGDp+tg4Z2Yr34Z05pA1mj/IGAMQlpeF/e67hZnAUAMCzoR1mDG4Nm2pG+HfOAPT5ajNuhkRL3I+aWmnwlJCcjruPYtC1Vd0K/NXKLzgsBhN/3CS4zujrYMbITmjjIThnDp4JwLbD/njyPBEjZq3FifUVv858+etOYfDTxsMZEwZ5w9rCGKHP4vD35tOIiEnC7+uOwbqaMYb3lnydsTAzRBsPZ7jVt4etjTmsLYyhqamB+BcpOHMtBPtP3kFmVi4+X7gD1UwN0bFVgwofF2XA+kx5fZAAaOPGjeWuo66uDiMjIzg5OcHV1bVCb42IlM3cvw8gKzsXmhrq2PHnZDR1LX0F3aapC5zsLLHon8N4EpmItbvP44sx0t8ESON/9wkOnr4DAPikTUP8+8s4aGgIuve51bdH57au6DZuKWISXmHRP37o4dMEJkb6UvdnbKiHerWqK5yPquSHP/cjs7hc9v09tUzTAK9mdVHLzhLzVhxCWEQCVu04V+5TM0muBoRh78nbAICu7Vyx5bcJwnLxaOCArl6N0GH074iOf4V5Kw6hdwc3ieUy/lMv2NeoBo8GghsGAKjWYnpF/uwq4ftl+0rL5n9T4dm4lnCZV/O6qGVvhbl/H8TjiAT8s/0cvplQgbK5U7Zstv0xsbRsGjqgm1cj+Iz8DdHxrzD3fwfRp6Pksln4jx8Sk9MAAOsW+qJvJw/hstbudeBe3x5jvtuAxOQ0/LL6CFb8NELhvCqTX6Z2gYGeNvLyCzBg9jbcEgk+LgdG4FlMMn6e9AnqOljis4Gt8MfWSwrtv7qFEWaP9gEAxCalouNn6xD/Ml24/M7DGBy8GIwzK8ejhoUxlnzeA96T10DSM9Z1B28iMj4Fdx/FCJvLvTr7cTTdfdOPf+0XXmd2/zkFzUTqs7bNBNeZBSsPIywyAat3ncdXYxW/zly7+wT7TwmuM53bumLjr6XXGfcGDujS1hWdxyxBTMIrLFh5GD3bi19nHGpUQ9CRhWWCU5Gl6O7dBCP6tEafyX8hL78Av609WuUDINZnyuuDDIIwevTocn9GjhyJvn37okmTJgx+6KMQGPoc/nefAACG9GxZJvgpMWmID5wdrQEA/+65hLz8AoXTWbXjHABAQ0Mdi74aKKz4SpibGuL7Kb0ACPqT7DpyQ+E0PiYBIZG4EhAGABjRu5XEdtFTh3eAi6NgKP7Vuy9UqFz+t/UMAEG5/P71ILFyqWZqiLlT+wAAUlIzse2wv8T9TBvREb07uAmDn49ZQHAkrtwpLps+rcrcLJSYNrwD6joJymbVrvMVKpu/RMpmyezBEstm3vTSstl6SLxsEl+mYs+JWwCAji3rl7lZKNG3kwc6tqwPANh17CYSX8rut6LM3OvWQDs3wbmy7fjdMsFPiRX/XcPDyBcAgCn9W0JTQ7FbjP7tXaGrLXguu3jzhTLBT4m4pDQs3nwBANCojg0+8XSWuK8V//nD73JouX2Fqrq7IZG4FiC4zgzr1bJM8FNiytD2wuvMut0XK3TOrNx2FoDgnFk8S/w6U83UED9+1hsAkJKWiR1+18X2oa6uLiX4KeXRwAFtm7kAAO4/ikZGZo7CeVUWrM+U2wcJgJ4/f47nz58jOVlyO3aij9HJS0HCz4O6S27LrK6ujgFdmgMQBCclAZO8MjJzcPXOYwCC197SXkl3824MIwNBm/3jl+4rlMbH5tjF0r9/aM+WEtdRV1fH4O6CcklJzRRexOSVnpmDS7cF5eLjWRc1rSX3RejZvomwXI5cuKdQGh+joxdLj8FwKU01BWUjOJ8EZfNYoTTSM3Nw6ZZgfjkfz3pSy6ZXezdh2fidFy+b45eCUFBQCEBw4ynN0F4tAAAFBYU4LlInVDU92tQTft5+IlDiOkVFwO5TgmNlZqyHtm6OCqXhUbeG8POZm9LrwrMiy3p71VcojY/NCZHv1JAeLSSuo66ujkFdi+uztExcC1CsPsvIzBGeZ17N60q9znT3Kb3OiNazihIdECAn7+0H1KgsrM+U2wcJgBwdHeHk5IRdu3Z9iOSIlMLN+08BCDp0Nq5rJ3W9Vu61RbZ5plAagaGRyMkVXCBaukkfMl5bSxMeDQWdFgNDIiv0lOljcT1QUC4Getpwqye9XNp4lD5ZvnHvqUJpBIRECMultbvscmlWPGpSQLBqlwsA+MtdNqXH9HqgYudMQHBp2Yju503aWppo3shRuM2bZVOSV8F+JL+FeHPZdQW/R8qkVSN7AEB6Vi4CH8dKXe/q/Ujh55au9gqlYWqkJ/ycmCz+9ke47FWG8HObxo4KpfGxuXFP8P0v/zpT+l2/oeB15m5I6XWmtVttqetpa2miaUNH4TYVqc9eJKfhavEDJ3NTA5ibGCi8D2XB+ky5fZAASE9PUKk1b978QyRHpBTCIhMAAI41LaApY+CA2vbWws9PIhIUS0Nk/ToOVjLXLUknv6AQ4VEvpK53I/AZOo3+Dc6dvkGdjl+j1cCfMeWnTThx6b7CA5ooo8fFx8zJ1lJmuTg7lJbLYwXL5XF46folTU/KSye/oBBPnycqlM7HpuS4lVc2Jc0TAeBxRLxCaTwKL13fpdyyEaQjqWweF+/H2FAP1hbGUvdhY2EifPIq+r2oalzsBSNUhccko6BQej3w+HmS2DbyyszOFX42ljLKHCAY4rqEvY0p9HQ+SHdmpfQ4UvA9LO+cqSNSn4UpWp9Fil5nZJ8zJdeh/IJCPJNxnRGVk5uPyNiX2HboGnpNWo6UtEwAwISB3grlU9mwPlNuHyQAqlmzJgCgoEC1n26+6cGDB1i4cCG6dOkCW1tb6OjowNDQEM7Ozhg9ejSuXxdvQ1ti3rx5UFNTE7anzc7Oxh9//AEPDw8YGRnByMgInp6eWLFiBfLzy3+FfPnyZfTv3x/W1tbQ1dVFrVq1MHnyZDx5Imhq4OPjAzU1Nfj4+MjcT3R0NL777jt4eHjAzMwMurq6sLe3x+DBg3H+/Hmp20VERAj/nk2bNgEA9u/fj+7du6NGjRrQ1NQsN21lkp2Th+QUwVPK6uWMlGJqrA99PcEr/9jE8oehFhWbmCL8XF46os0WZKXzPO4lHj6NQ1Z2LrJz8hAVlwy/c4EY//0G9P/sb8S9SJG6rbLLzsnDyxTBk+XyRrAxNdaHQXG5xCQoVi4xIse3hpXkJgklRJssiJanqilTNtamMtd9q7JJSBF+Lu87UFMkH2+mU/J/eUZCKiljRfOqLHS0NGBhKngSHytleNwSr9OzkZ4lCGRqWkm/kZJENHhq01j6MLutRZapq6uhhqVi6XwsylxnLE1krlvmOiNyDsgjVuR7W+51RrQ+k/F9vxoQBpvWn8Om9edw8PkKLT79GbN+242IGMF3YEDnpvhseEeF8qlMWJ8pvw/y2KRz585YtWoVrly5gpYtpbctVCUXLlxA+/btxX6fm5uLJ0+e4MmTJ9iyZQu+/fZb/PrrrzL3lZCQgC5duuDevbLtOm/duoVbt27h1KlTOHjwINTVJce7ixYtwo8//ljm6X54eDjWrFmDHTt2YO/evXL9TevXr8f06dORlVV2rPuoqChERUVhz549GDduHFavXg1NTelfvaKiIowaNQpbt26VK11lJNpx00Cv/GEt9XW1kZmVi4ys3HLXrWg6JRc/ABLT0dLUROe2rvBqXhd1a1WHsaEuXqdlISA4AlsOXEVsYgpuBYVj2BercGj1TBgb6ontQ9mlZ5bOxSRfueggIysXGVmKdcRNzxAtF9kT3ImWS3oV7vD7thQuGz1B2Sh6zMqkU86Qs/oi+XjzO1CSrjzD1pZ8BxT9HikL0b9RnjoqMzsXhnraMFRwcsdjVx/hy2HtAADfjPTG6RthyMkr++BUR0sD34ws+2bAUI7vy8covcLXGUXPGQWuM7qi1xnFv+921c3x+9eD0L5l1e7bxfpM+X2QAOjzzz/Hpk2bsGTJEgwdOlT4RkiV5efnw8DAAD169ECHDh1Qr149GBsbIzExEcHBwfj7778RGRmJxYsXw8XFBWPGjJG6r/79+yM0NBQzZsxAr169YG5ujkePHmHBggUIDQ2Fn58f1q1bh0mTJoltu3PnTsyZI5ikzczMDLNnz4aXlxcAwVuhxYsXY8iQIbC0tJT592zYsAHjx48HALi6umLSpElwd3eHvr4+wsPDsX79ehw7dgzr16+HiYkJli5dKnVfy5cvx/3799GuXTtMmTIFLi4uSElJQURERHmHVWnk5JbOf6Elx7w52lqCUzE7R7F5M7IVSKckDWnpHFn3hcShMVt7OMN3QDtMmrMRF28+QlhEAv7ceAJzp/dTKK/KoKStNABoyTEJnrZ2xcpFtPxFj7skOuWUi6rIziktm/KOmeg6ipeNSDoyHsQAgI526fKsN+a0KTn3FPkeZVXR8tUVOQ65eeW35MjNFayjq6OlUDp3Hsbg2NWH6N6mHhrVscGRP32xYP053AoRzAPUvIEdfhzXAY3q2CAnN19YPqraBK7MdUaOc0bnndRn5VxntOWrz9zq2+P81tkABN+pmIRknLz8AHtP3sbMX3Zg9oTu5c5Zp8xYnym/D1JrODs7Y8eOHRgxYgRatmyJ3377DZ9++im0tRV7OvQxcXNzQ3R0NExNTcWWdenSBdOmTUPPnj1x+vRpzJ8/H6NGjZI6PHjJWx7RJmIeHh7o0qULGjRogISEBPzzzz9iAVBOTg5mzpwJQDD5rL+/P1xcXITLW7Vqhb59+6JVq1Z4/Fj6yCRRUVGYPl0wL8no0aPx77//lnnD4+7ujv79++OHH37AL7/8guXLl2PSpEll0hJ1//59jBo1Cps2bSp3yExJoqMlT4xXIi4uTuF9KkpHu/TCL09H0NzikW4UvWHQVSCdXJHRdCSlI2tuIEN9Xaz62RdtBi/Eq9cZ2H7YH99N7iVXxa5MRC8AeXLdyFWsXETLP7ecUYxyyikXVaGrI3qTXX6z3YqeM6LfgdxymgeL3lzo6ZZNR1dbC5nZuQp9j/SqaPlmi95kyXWDJFinIgH9lN8O4r/Fw+HZwA7N6tvi0JJRYuvceRiD0PBEjOjmDgBIz1TszfnHosx1Ro5zJued1GflXGdy5avPDPR0UL926ah/TerZobt3E3zatRlGzFqLL3/dhbgXrys0Z5EyYH2m/N55HyB1dXVoamoiJCRE+LsOHTpg+fLlsLS0RExMDEaOHAkTExM0btwY3t7e6NChg9Sfjh2rbhtQWSwsLCQGPyW0tbXxxx9/AAAiIyMRGBgodd3p06dL7B9jbm4ufHN0//59vH79uszyAwcOIDFR0BFu7ty5EgMSFxcXzJ0re/K4v/76C5mZmahRo4bM5m3z589HzZo1UVhYiC1btkjdn6mpKVasWFGh4AcA7OzsZP54ekoekvpdMtCX/qpZkpLOv+U1l3qbdDJFmq4omg4g6BzZu6O7cF/3H0YpvI/KZqhf2rFavnIRrCNPE4Yy6RjI32RItFwqOgv4x0DhssmSv8mG1HTKaW6SmSW96U9JuvI0WSn5Dij6PVIWZZtAlV93lDSDSs9WPDBJzchBzy824buVJxAaUbajdvzLNPy+9SK6z9wII5FzLCU9683dqATDCl9nFD1nFLjOZIteZxT/vrdrVhfjiwc/WLrhhMIDNigL1mfK7708vn1zpKgLFy6UuZktKipCTk4OHjx4IHUfampqKCoqqvBNcFWTk5ODhIQEpKeno7BQMBa76HG8d+8emjZtKnHb4cOHS92v6Dbh4eFwc3MT/v/sWcHEZurq6hg5cqTUfYwYMQIzZ86UOgLYoUOHAAC9evWCrq70kXs0NTXRqlUr7N27F/7+kid9LNmPkZGR1OVVga6OFsxNDZCckoG4cjq2p6RmCm+Cy+sw/ybRDqlxiSloUk/6sLOiHewVTaeE6Gg18S9ey1hTOenqaKGaqSFepqSXO+BASmqmsKKXNreCNG8OOOFeX3q5iHYklacD6seqTNmU00n7rcpGpCNwbGIK3BtI72wv2sH4zXRqWJsiMTlNroErSspY0bwqi5y8AiSlZMDC1AA1ZIwQBQAmhrowLOnQXcFJSPPyC7F6/w2s3n8DxgY6sDA1QEZWLhJEhsZuWEsw4lVqRg5iXlTtCRkrqsx1ppz6uMx1ppxO+W968zrjJqM+Ex34oEYFv+9dvRph5fazKCwswrGL9/C5Y+cK7acysT5Tfh+k/YqXl5fKBDKKyMjIwN9//41du3YhODhY5ih5SUlJUpfVq1dP6jJz89LZ49PS0sosKwlAnZycYGYm/Ytsbm6OWrVq4elT8THfX79+LRwpbs2aNVizZo3U/YiKj5c+1GPjxo3l2oc0UVGy30zExcV9kLdAzg7WuJHyDBExScjPL5A6DObT5yJDjJYzjOWbRIe9fBIpewjlknQ0NdThaKvY8LQlPoZhsF0creEfmI7w6BcyyyVMZOjX8oYXfVPJzN5A8ZCzMkZzLUlHU0Mdtexk97X72Lk4WcP/bvllIzpUrGhQLo96TqJDziagh4x1w4qHGJZUNnWdqiMwNAqp6VlISEqVOnRsfNJrpGUIOiq7OCn2PVImj58nwcLUAE41zaGhriZ1KGzRoa9FR3WrqNSMHKRmlH0qbWlmgFo1BNe2u49i8BFUSxXm4mCD6ylPyz1nnojUZ+UNzf+muiLnmOh+JKcjuA5paqjDqYLXmWqmpXP/RMdX3ZHGWJ8ptw8yDPaFCxdw/vz5Cv98jCIiItCoUSN8//33uH//frlDhL85spoofX3pfTdER357M41XrwQVi5WV7PljAEgdBKGkCZ2iMjMzpS6TFYzJw9bWVuZP9erV32r/8mreuBaA4uZij6QHZf53SwPL5o2cFEqjSX17YZv864HSZ07PzctHQHCkyDYVe/Yh2hxB1lwByqxFE0G5ZGTlIlBGM76rIrOltyguS3m513cQHuNrd2WXy+0HEYJtGjhUuT5V71rLJoJJFssvm9JjWlKe8hI9zqL7eVNuXj5uBUWIbVOa19J0Rb8r4nmt+PdImVx/8BwAYKinDTeXGlLXEx2++kbxNu/agPauUFcXPFQ9eDGknLU/bp5NBNeM8q8zpd91z0aKfQ/dRK4z1wKlT36Zm5ePO8ERIttUrD4TfZulX4Hm2sqC9Zly+yABEIkbOXIkwsPDoaamhrFjx+LUqVOIiopCdnY2ioqKUFRUVCZgUdYn76J5nDlzJoKCguT6OXnypNR9Shvsoarp0q6R8POeYzclrlNYWIh9J28BAEwM9dBaxgzMkhjq66JNU0HfrSu3H0t9fX384n3hU5uuXhV7w5aanoXD5+4CAPR0tdFYRnM7Zdbdu/Tv33lE8lxbhYWF2H2suFyM9NC2meQBO6QxMtCFV/E2F28+kjpfwpHz94Tl0sP77d58fgx6+JQeg+1+kpvJCspGcD6ZGOmhXUXKpnlJ2TyUWjZ+5wOFZdOzfROx5d28Ggtvwnf4SZ+zbaffDQCC+Wq6VfDcUwZHrzwUfh7e1U3iOmpqwODOgmOVkpaFy4ER7zwfRvra+HxIG2Eae88GvfM0qhLR+nzX0RsS1yksLMSeE6X1WZumCl5nDHSFdeDlW4+kXmeOXSi9znR7i/rM71yg8LPoQAlVDesz5cYAqBI8fPgQV65cAQB89913WL9+PT755BPhZKglSt7QvC8lb1rkeYvz4oXkGZ2rVasm/JyZmQlXV1e5fqSNAPcxcW/gIHyas+vIddx5EC62zppdF4RvVcYO9BIbynrPsRuwbTsTtm1nYun64xLTmTxUMJ9UfkEh5izbi4KCwjLLk1PS8csqPwCCIGtoT/G5uM5fD0VWjvQOy+mZ2Zjy0ya8ei2YdG9IzxZlRp+pSpo2dEQrN8GTuW2H/XErSLxcVm4/J2yWMGmwj1i57DhyHdVaTEe1FtPx27pjEtOZOqIDAEG5fPPHf2Ll8jIlHfNXCvrPmRjpYWSf1m/3h30EmjZ0RCv34rI55I+b95+JrbNi+znh7OeTh0goG7/rMGs+DWbNp2Hx2qMS05k+QjC4Tn5BIb7+fY/Espn3v9KyGSWhbKwtjDGwa3MAwNnroTh09q7YOgfPBODs9VAAwOBunlX2rSkABDyKxbX7grfII7q5o3kDW7F1pg1sjXoOgtYCq/ffQP4bx3VolyZ4dXYuXp2di9mjJLcLtTY3hKaG5FsTQz1tbJ43CDbVBH1Ef1xzWjjpqqryaOAgfNOww+86bkuoz1btPC+8zowf6C12zuw6ekM4Kekf/0q+znw2tLQ++26J5PpswT+HAQjOmeEShrA+cOoOUssZsOLQ2bvYeugaAMHAO13aucpcX5mxPlNu7+0Ohn1+pAsODhZ+HjJkiNT1bt++/V7z0bBhQ1y/fh3h4eFITk4u019IVHJyMp49Ez9xAUHTuJo1ayImJgZnzpxRqYEr5DH/8/7oO+UvZOfkYdgXqzF9VCe0cndGdm4eDp8JwPbDgqdCtewsMWmo+MS48mjT1AV9Orrj0Nm7OHXlAYZ+sQrjB3nD2sIYD5/G4e8tp4VPhb6b3BOmxuJNJlduO4PpP29FN+/G8GxcCw41LGCgry02ESoA1La3wpdVdGjSEr98OQDdJ/yJrJw8DJixEl+M7oy2TZ2RnZOHA6cDsPngVQCCv/WzYR0qlIZXs7ro/4kH9p8OwInLQeg/fQUmD2kPG0sThDyJxZ+bTgrbt//0WW+J5QIAQY+jEfRY8tDuCS9TseONt1i9O7hX6dHkFn/1KbqOWyYom+kr8YVvZ7Rr5oLsnDzsO3UHmw8IyqaOvRWmVnCmeK/mddG/c1PsP3UHxy8Fod+0FZg8xAfVi8tm6cbSspk7rY/UspkzpRfO+ocg6VU6xv+wEXdDngtv2E5efoCV2wUDzViYGeKHKT0rlFdl8u3KEzjx11jo62ph328j8OeOK7gcGA5dHS0MaO8K356CQXfCopKw8j/pA93IMrBjI0wb1Bq7Tt3D1XsRiE9Oh5G+Dpo3sMW43s1gV9zpe9vxu9h2XPwmrYRrbWs0qiO5P4WVuSGGdin7FPzwxRBkZFfNeU0WzOyP3pOXIysnD4O/WIXPR32CNh7OyMrJw6EzAcKAora9FaZU8DrTtpkL+nbywMEzATh55QEGff4PJg72hrWFCUKfxuKvzaXXmR+m9JJ4zmw5dA2zft+Nbu0aoaVbbdS2t4KxoR4ys3PxJDIBR87fw1l/QZNGNTU1LJjZH2bGBmL7qUpYnymv9xYAde7cGVpabz9GuJqamsTO91VZvshY7bL6wqxevfq95qNjx45Yv349CgsLsW3bNsyYMUPietu2bZPZBK93795YtWoVnj17hr1792LgwIHvK8tVjquLLVbNH40ZC7YhLSMbi9eIP8GpZWeJzX9MLDOcpaKWfD8UaZk5OOcfgmsBYbj2RhtedXU1fD66M0b0bSN1Hympmdjpdx07Zbz+buFWGyvmjqzyF6XGde3w76IxmDx3C9IysrGw+A2ZqNr2Vti1bDKMDCpeLn/PGY60jGycvhaCK3fCcOWOeLnMGtsVvv3bSt3HsYv38buUp7JPIhMxfcH2Mr9r6+FcpQOgxnXtsP6XsZj002akZWRjwT/iZVPH3gq7l095q7JZ8WNx2VwNxuXbj3H5dtm5ztTV1fD1uK4YI6NsbG3MsHPZZIyYtRYJL1Px15bT+GvL6TLrWFczxrYlE6v8iEkAEPQkHuMW7sWab/vB2FAXP40Xv2ELi0rC4O93vNWbGWtzQ3w+pI2wqZuovPwCrPzPHz+vPytzHz3a1MO3o30kLnOxt8A/3/Qt87srgRHIyK56I1sCQKO6tlizwBdT529FWkY2fll9RGyd2vZW2LZkIgzf4pz584ehSMvIxln/EFwNCBPrK6KuroYvfLtglIzrTEZmDvaevI29J6U/4DU10seiLwdgQJdmFc6rsmB9przeWwAUExPzTvbzMb5NcHYubX+7efNmtGjRQmydVatW4eDBg+81H/369YOVlRUSExMxf/58dOvWrUzeACAsLAzz58+XuZ+vv/4aGzZsQE5ODiZPngwnJyc0aya94jp27BhsbW3ferS3quKTtq44vfkbrP/vIs5eC0Hci9fQ1tSAo60FerR3w5gB7aCn+3YdPfV0tLHlj4k4cOoO/jt+EyFPYpCangULMyN4NqmNMQPaoqmr9AEWfpzWB1duP0ZAcASePk9E8usMpKZlQU9XG9YWxnBv4IA+nTzg7Vnvozknu7ZrhMvbv8Oa3Rdw6mow4hJToKWlASdbS/Tp6I7xA72E85lUlJ6uNnb9OQV7T97GziM3EBwWg9fpWbA0N0Irt9oYP9BL4YEvVEE3r0a4svN7rNl1HqeuBCO2uGxq2VqiTyd3TBjk/U7KZs/yKfjvxC3sPHIDD8Ji8DqttGwmDPKCpxydfJu5OuLqru+xeucFHL14D1FxyQAA+xrV0N2rMaYM9YG5qeFb5VWZnPB/jLYTVmPSgBbo3MIZNSyMkZdfgGcxyTh0KQTrDt5EVk75Ez9K43clFDramvByd4JTDTNYmBogN68AsS9Sce7OU2w7dhcPIyU3yVZlndu64tyW2fh3z0Wc8Q9BbGIKtLU04FjTAr06uGPsp+3e/pzR0cb2pZOw/9Rt7D56E8HF1xlLcyO0aFIbYwe0QzMZ9dk/80bi0q3HuBoQhtAnsXiRnIaXKenQ0tKAuYkh6teujvYt66P/J02lvqWoilifKSe1onfcu15dXR1qamqoXr36O3kDBAjmr/mYFBUVoXHjxsJhqIcOHYrhw4ejevXqiIqKwrZt27B37160adMGV68KXo/OnTsX8+bNE+5j3rx5wsBEVhFeuHAB7dsLXnmfP39ebMLUnTt3YtiwYQAEfYK+/fZbeHl5AQAuXbqE3377DYWFhbC0tERYWBh8fHwkjsy3adMm4aSr2traGDlyJHr27Al7e3vk5+cjOjoaN2/exN69e/H06VP4+fmhZ8/SV6gRERFwchJUnBs3boSvr6+8h1Nh0dHRsLOzAwDcfxSOGjXF27JT5dKTY7Z5+vBKOsmS8jHrKPtBFVWe+BNzKjsLJIEOrzNKJzo6Gs5OgvuzqKgo2Nq+v/uz9/YG6NSpU2jQoMH72n2Vpqamhq1bt6JDhw549eoVdu7ciZ07d5ZZp1GjRvjvv/9Qo8b7HQFl6NChePbsGX788Ue8evUKs2fPLrNcX18f//33HxYvXoywsDCpE536+vpCT08PEydORGpqKtavX4/169dLXFddXR0GBlW7CRURERERVU0cBa6SuLm5ITAwEJMnT4aDgwO0tLRgbm4OT09PLFmyBDdv3vxg89X88MMPuHjxIvr27QsrKyvo6OjAwcEBY8eOxe3bt9G9e3ekpgpm2jYxMZG6n8GDByMiIgKLFy+Gj48PrKysoKWlBX19fdSqVQu9evXCsmXLEBERIXwrRURERET0Ib23JnBBQUF8A/SRyMvLg4mJCbKysjBnzhwsWLCgsrP0VtgETvmxCZxyYhM45cUmcMqLTeCUE5vAKZ8P2QSOb4CoXAcPHkRWlmDs/pYtxeeQISIiIiKqKhgAEZ48eSJ1WUREBL788ksAgLW1Nbp06fKhskVERERE9M5Vzanc6Z2qV68eunfvjp49e6Jhw4YwMDBAYmIizp8/j9WrVyMlJQUAsGTJEmhq8itDRERERFUX72YJBQUF8PPzg5+f+ARdgKBf18KFCzFixIgPnDMiIiIionfrnQdAJXP21KxZ813vmt4TPz8/HD9+HNeuXUNCQgJevnwJHR0d1KxZEz4+Ppg6dSpcXV0rO5tERERERG/tnQdADg4O73qX9J717NmzzKSkREREREQfKw6CQEREREREKoMBEBERERERqQwGQEREREREpDIYABERERERkcpgAERERERERCqDARAREREREakMBkBERERERKQyGAAREREREZHKYABEREREREQqgwEQERERERGpDAZARERERESkMhgAERERERGRymAAREREREREKoMBEBERERERqQwGQEREREREpDIYABERERERkcpgAERERERERCqDARAREREREakMBkBERERERKQyGAAREREREZHKYABEREREREQqgwEQERERERGpDAZARERERESkMhgAERERERGRymAAREREREREKoMBEBERERERqQwGQEREREREpDIYABERERERkcpgAERERERERCqDARAREREREakMBkBERERERKQyGAAREREREZHK0KzsDBBVJk11dWhp8DmAslFXV6vsLBBVKXEn5lR2FkgKm15LKjsLJEH0wa8qOwv0hqyc/A+WFu/8iIiIiIhIZTAAIiIiIiIilcEAiIiIiIiIVAYDICIiIiIiUhkMgIiIiIiISGUwACIiIiIiIpXBAIiIiIiIiFQGAyAiIiIiIlIZDICIiIiIiEhlMAAiIiIiIiKVwQCIiIiIiIhUBgMgIiIiIiJSGQyAiIiIiIhIZTAAIiIiIiIilcEAiIiIiIiIVAYDICIiIiIiUhkMgIiIiIiISGUwACIiIiIiIpXBAIiIiIiIiFQGAyAiIiIiIlIZDICIiIiIiEhlMAAiIiIiIiKVwQCIiIiIiIhUBgMgIiIiIiJSGQyAiIiIiIhIZTAAIiIiIiIilcEAiIiIiIiIVAYDICIiIiIiUhkMgIiIiIiISGUwACIiIiIiIpXBAIiIiIiIiFQGAyAiIiIiIlIZDICIiIiIiEhlMAAiIiIiIiKVwQCIiIiIiIhUBgMgIiIiIiJSGQyAiIiIiIhIZTAAIiIiIiIilcEAiIiIiIiIVIZmZWeASBVExyfj3z2XcOZaMKITXkFHSxOOthbo09EdvgPaQV9X+52kc+D0Hew6egMhT2LxOi0TVubGaOFWC2MGtEMzVyep2+Xk5uHstRAEhEQiMPQ5YhJeITklA+mZ2TAy0EVteyt4Na+LkX1bo4aV2TvJqzKIik/G2l0XcOpqMKLjX0FbWxO1bC3Rt5M7xg30emflsu/Ubezwu47gsFikpGXCqpoxWrnVxviBXmjeSHq5iEpOScea3Rdx9OJ9PI99CQCwr1ENPbwbY9Jgb5ibGr6TvCoLlo3yio5Pxvr/LuHMtRDEiNRnvTu6Y3T/tu+sbA6eDsCuozcQ+lRQn1maG6NFE0F91tTVsUL73LTvMr5bulf4/+U/DMPgHi3eSX4rm62lESb2aYbOnrVha2WE3LwCPIt9hYOXHmH9kQBk5eS/dRp17athbA93tG1sD1srY+hqayI1IwehkS9w/PoTbD1xH+lZueXup0+7uhjYvgHcnG1QzUQfhYVFSHyVgYBHcdh+Ogjn7oS/dV6VSUzCK2zYewln/YMRk5AC7eJzpld7N4zu1xZ67+icOXQmAHuO3UDo0zi8Ts+EpbkRPBvXxuj+bdG0oaPMbVsOnI/o+FflpmFrY4br/819J/mtTGpFRUVFlZ0Jog8pOjoadnZ2AICQsEjUtLV9r+mdvhqMz+ZtQWp6lsTldeytsH3ZZDjWtKhwGtk5eRj/wwacvhoscbm6uhq+HtcNX47tKnF5eNQLtBy0oNx09PW08dusQRjU3bPCeZWHtub7fzl98soDTPxxs9RycXawxp7lU+Bo+3bl4vvtepy88kDicnV1NXw7oTu+Ht9N5n4CgiMxfNYaxCelSlxe3dIE25dMhHsDhwrnVZmwbBSXnVfwXvdf4szVYEydv1Vq2dS2t8L2pZPg8Jb12cQ5G2XWZ1+N64Yvx3RRaL/xL17Da9gvSMvIFv7uQwRA1Xstea/7B4DOnrWw9pteMDHUlbj8cdRLDPrxP0TGv65wGlP7N8fcsd7Q0tSQus7zhNcYNm8fgsNfSFxurK+NbXP7o10T2efD/guhmLzkCPLyCyuc3/JEH/zqve1b1NlrwZi+YCtS07MlLq9tb4Utf0yEQ423O2cm/7QJZ65JP2e+HNMVM32lnzPKEADFxkSjcT3Bg6eoqCjYvsf7MwZAKuD58+dYtGgRzpw5g5iYGOTk5AAADhw4gL59+1Zu5irBhwyAgsNi0GPin8jKzoWBvg5mjPwEbZo6IzsnDwfPBGDboWsABDd0JzfMgoG+ToXSmTJ3M/afugMAaNPUGRMGecPGwgShT2Px1+bTiIhJAgAs/W4IRvRuLbZ9eNQL9Jy0HG2aOsO9vj1sbcxgXc0EmpoaiH+RgtPXgrH/5B1k5+ZBTU0N25dOQsdWDSp4VMr3vgOgoMfR6DpuGTKzc2Gor4OZvp3Rrrhc9p+6g80HBeXi4miNs5u/gWEFy2XCnE3Ye/I2AKBdMxdMHuIDGwsThDyNxbKNJxEeLSiXv34YhlF9xcsFAGITU9B+5G9ITE6DpoY6PhveAV3augIQBAr/bD+H/IJCWJkb4fzW2ahhZVqhvCoLlk3FfIgAKDgsBr0mLRfWZ9NHdkIbj9L6bPthfwBAHQdrnFj/VYXrs8/mbcGBkvrMwxnjB3nDxsIYoc/i8LdIfbbk2yEY3ruV3Psd+916HL94HxZmhkh6lQ7g4wiAXJ0sceLPETDQ1UZaZg6W776Oy/efQ1dbE/2968O3uxsA4NHzl+g4YzMysvMUTqNvu3rY+EMfAEBObj7+PXIXF+9G4OXrLDjVMMW4nu5o5Sq4rsa9TEPLCf8iNVP8TdCenz/FJ561AQARcSn4394bCIl4AU0NDbg52+DzgS1gYaoPAPjXLwBfrzxdkUMilw8RAIU8iUGfKX8Jzhk9HUwb0QmtPeogOycPh87exQ6/knPGCkfXVvycmfbzVhw8LThnWns4Y9ynXrC2MMHDZ7H439YziCw+Z37/ZjCG9ZJ8zpQEQF3auuLrCT2kpqWtqYFa9lYVymd5PmQAxCZwH7nnz5+jadOmSEpKquysqKQ5y/chKzsXmhrq2L38szJNato1c0EtW0v8vPIQwiITsHrnOXw1TvbTZkmuBYQJg5/ObV2xafF4aGgIAgj3Bg7o0q4ROo/5A9Hxr7BgxWH0au8GEyP9MvtwqFkND44uhJqamoQUHNDdpwlG9mmD3pOXIy+/AIvXHHmvAdD79v2yfcgsLpd9/5sKz8a1hMu8mtdFLXsrzP37IB5HJOCf7efwzQTFy+XqnTDhDXbXdq7Y9sdEYbl4NHRAN69G8Bn5G6LjX2Hu/w6iT0fxcgGAhf/4ITE5DQCwbqEv+nbyEC5r7V4H7vXtMea7DUhMTsMvq49gxU8jFM6rMmHZKK+f/tovrM92/TkFzUTqs7bNXFDLzhILVh7Gk8gErNl1XuobZ1mu3X0iDH46t3XFhl/HCcvGrYEDOrd1RZcxSxCT8AoLVx5Gz/ZNJJbNm05cCsLxi/dRzdQQU0d0wvz/HVQ4b8rql8kdYaCrjbz8Agz4YQ9uhcYKl12+9xzPYl/h5/HtUde+Gj7r3xx/7LimcBpfDyt9CDBq4QGcuvlM+P+Ax3HYdyEUm+f0Re+2dVG9mhFGdG2Cf/bfKrOPJnWshcFPeOwreE3dVKa53JX7z3HwUigu/zMWpka6GNPdDYu3XcHL15LfNlYFc/8+IDxndiybjKYiTdHbNHWBk60lFq06jCeRiVi7+zy+GKP4OeN/94kw+PmkTUP8u0jknKlvj85tXNFt/FLEJLzColV+6OEj+5wxNtRDvVrVFc5HVcNBED5yCxcuRFJSEjQ1NfHbb7/B398fQUFBCAoKQseOHSs7ex+1uyGRuBbwBAAwrFcrif0JpgxrDxdHawDA2t0XkZev+FPcldvPAgA0NNTx29cDhRVfiWqmhpjzWW8AQEpaJnb4XRfbh7q6upTgp5RHQwe0a+YCALj/KBoZmTkK51UZBARH4sqdMADAiD6tytxgl5g2vAPqOtkAAFbtOl+hcvlr6xkAgnJZMnuwxHKZN13wRDUlNRNbD/mL7SPxZSr2nBDcRHRsWb/MDXaJvp080LFlfQDArmM3kfhSclOsqoBlo7wCReqzob1algl+Skwe2h7OxfXZugrWZ/9sK63Pfp1Vfn22U0J99qb0jGz8sEzQ7+en6X1gZlx+wFRVuLvYCJuTbTt5v0zwU2LFvpt4GCl4CDqlXzNoaih262ekr40GTpYAgMCw+DLBj6jft18VfvasX1NseYuGpU/zVx28LbGvUPSLNGw/HQRA8B1oVreGQnlVJoGhz+F/V3DODOnZskzwU2LSEB84OwjOmX//u1Shc2bVjnMABMdr0Zfi54y5qSG+n9wLAPA6LRO7jt5QOI2PEQOgj9yZM4ILfd++ffHNN9+gZcuWcHV1haurK4yMjCo5dx+345eChJ+H9JTcxEJdXR0Duwn606SkZeJaQJhCaWRk5uDy7ccAAO/mdaUOUNDDpwmMDARtw49euKdQGqL09Uo7aubkvX2H2spw9GLp3z9cSlMAdXV1DC7u55SSmokrdx4rlEZ6Zg4u3XoEAPDxrIea1pLLpVd7N2G5+J0XL5fjl4JQUCBoAz+sV0up6Q3tJfh+FRQUlvneVTUsG+VVpj6T0mRMXV0dA7s2B1Dx+qykPL2a15XaZLC7T2Nh2Ry7eL/c/f6y+ghiE1PQ2qMOBnV7v/0XP7QerZyFn7efkvz9KioCdp8V9HUzM9JD28b2CqUh2ucnIj5F6nrhsaXLdLTE+wlpi+wnUsZ+IuJK+6FoS9hPVXHycml5SPveqaurY0DxOfM6LVMYMMkrIzMHVwME50y7Zi5Sz5lu3qXnzHE5zhlVwADoIxcTEwMAcHFxqeScqJ4b954CEAQNTeraSV2vlXsdkW0kP1mTJiAkEjm5+WL7eZO2lqZw1KS7IZEVesr0IjkNV24LbmiqmRrA3MRA4X0oA/9AQbkY6GnDrZ70cmnjUXo8rwcqWC7BEcJyEd3Pm7S1NNG8kaNwmzfLpSSvgv04QxrRZdfvPZW6nrJj2SivkrpJX08bjeWsz27eV6xs7orWZ261pa6nraUJj+IRrcqrzwKCI7D5wBVoa2lg8axBCuWnKijpd5OelYvAsHip610NihJ+btlQsX4VyalZSE4VNENztDGVup5TjdJlT2KSxZY/Ffmdg4z9OFYvfSjxVMJ+qoqb90vvAWSeMyLfdUXPmcDQ0nOmpZvs+syjeCCWwNCK3QN8bBgAfeRycwWvmLW0tCo5J6onLEJwMXKytYSmjFFzSl5/i26jaBqAoOOxLCXL8wsK8SwqUa795+TmITL2JbYeuoaeE/9ESlomAGD8IB+F8qlMHocnACi/XFwcbUq3UbBcHoWXrl/SxFEaZwdBOvkFhXj6vGy5PC7ej7GhHqwtjKXuw8bCRPh0r+Tvq4pYNsorLFK++qxOmfpMsb83LLJ0/fLrM0EnbEF9JnnEsbz8AsxavAuFhUWYMqyjsHnex8TFrhoAQZ+agkLpY1o9jnpZuo19NYXT2Xw8EADg5myDTs3Em6YCwKzifkL5BYXYekL8LcPZO+HCNz+T+zaDvo74fUkNCyMM+0QwkMjNkBiERFTd/ssl33/HmhYyz5naIt/1J5Fvcc6UMzBBbZF7gHAp5wwgeNjRafRvcP7kG9Tp9DVaDfoZU+ZuwolL9/ExjZvGAOgjtGnTJqipqZXp0zF//nzh79TU1ODr61tmm6KiIuzduxcDBgyAnZ0ddHV1YWZmBk9PTyxYsAApKSky07x+/TrmzJkDHx8f2NjYQFtbG8bGxmjQoAGmTJmCkJAQmdv7+vpCTU0Njo6OAIC4uDjMnj0bDRs2hJGREdTU1HDhwoUKHI3KkZ2Th5cpGQCA6pamMtc1NdYXNi2LSUxRKB3R9csbYaqmyPLYBOnpXA0Ig3WrGbBuNQP23l/Bc8B8zFq8Szjy0oAuzTB1eAeF8qksBOUiGP2phrWpzHVNjfVhUFIuCeUPDSoqRuT4llsuIvl4M52S/8szelhJUy5F86osWDbKKzsnD8nC+sxE5rqi9ZmsekaSWJHjU73csil9SxAr5bj+s/0sQp/GwaFGNXzu+4lCeakKdLQ0hCOmxSalyVz3dXqOsM9NTUvFm78v3emP8wERAICtP/XDz+Pbo1OzWnB3sUE/r3rw+30o+rarh/yCQny76kyZgKtEbl4BJv52BMmpWahVwwyXV42Bb/cmaNGgJto0tsPU/s1x/n+jYWakh8j4FExddkzhfCqL7Jw8JL+W8x7ASOScSVSsjogVuQco75wRratkpfM87iUePotDVnYusnPyEBWXDL9zgRj/wwb0n/o34l6kSN22KuEocIQXL16gX79+uHr1apnf5+Tk4NatW7h16xZWrlyJQ4cOoUUL8bbfmzZtwpgxY8R+n5eXh9DQUISGhmLdunX4+++/8dlnn5Wbn+vXr6NXr15VeuS69MzS8f7lGdZSX1cHmVm5Cg8skCEyn4WBnux0RPvvZGQpPoCBXXVz/PHNYLQv7tRdFZUpl3KOFwDo6+kgIysX6QqWiyLlry+SjzfLpSRdeYZ6LgkIKlK2yoBlo7xEj7FcZaOrLajPFPx7FUlHdLJVSemER7/A8o2nAAC/zBoIPZ13M9GkMjHUFz0G5Q9tnZmdB0M9bRhWYNLNjOw8DJyzB8M6N8LMQS0x/VNPTP+0bL+Ww1ce4e//buDOozip+7kZGgPvqRsxsU8zTOrTFH/OKDvqWVpmDn7Zchnrj9wVNrurijIqfM6UP4lsRdMpe86Ip6OlqYnObV3h1bwu6jpVh7GhLl6nZyHgQQS2HLyK2MQU3AoKx7AvVuHQ6pkwNtRTKK/KhgHQR6hv375o1qwZAKBRo0YAgClTppQJPszMBE/PMjIy4O3tjdDQUGhra2PMmDHo3r077OzskJGRgUuXLmHZsmVISEhAt27dcPfuXTg4lJ3ALD8/H2ZmZujduze8vb3h7OwMAwMDxMbGIiAgAH///TeSkpIwbdo01KtXDx06SH97kJ6ejgEDBiA7Oxs//PADPvnkE+jr6yMoKAjVq8s3LGN0dLTM5XFx0ivnd6WkTS5QtuOnNDraglMxO0ex+RmyRdLRKqezqLZW6ekuKx23+va4sO1bAEBuXj5i4l/h5JUH+O/ELXy+aDtmT+ih0NwbyiRbZCZ00eMhTck6ipZL2fKXnU5J2QNA1hvzc2TnCv5fXtkCgHbxfrIUzKuyYNkor5zc0nxryVM2FazPRNMpr/O7trbs+mz273uQnZuHXh3c0KEKP7SRRVekLHLl6NORWzxXlK5OxW793F2qY4B3fan9gHzcHfHiVQYeR71EmoQ5gEr0blcPfdrVlVjGRvo66O9dH1GJqdh1RvIkxVVB2XNGjjqigvVZtgLplHfOHFn7hcThsVu7O8N3QDtMmrMRF289QlhkAv7ceAJzp/dTKK/KhgHQR8jU1BSmpqZlfmdlZQVXV1exdb/99luEhobCxMQEZ86cEQZOJdq2bYvhw4ejVatWiIuLw5w5c7B169Yy63Tr1g3Dhg2Dvn7ZE8fd3R09evTAjBkz4OXlhfv372Pu3LkyA6CXL1/C0NAQV65cQZMmTYS/b968ubx/vnCS08okeuMkz4Wp5KZMV0KbaFl0RdLJK2cixFyRUdtkpWOgp4P6tUuHHm1Szx7dfZpgQJdmGDFrDb78dSfiX6RUaM6iyiZ64c+VYxS7knUULZey5S87HdEbcj3dsunoamshMzu33LIFgNzi/egpmFdlwbJRXjrapfnOk6dsKlifiaaTW159liu9Ptt99AYu334MIwNd/Px5f4XyUJVk5yn2oK0k4BB92CCv3m3rYs03PaGrrYkHzxLx69YruBYUhfSsXNS0NEJ/7/qYNbQ1xvXyQOtGduj73W4kvsoosw81NWD9d33Qz6seAGDLiXv41y8Aj5+/hIa6OhrVtsKMgS3QvZUzVs3qgYZOlvhx3XmF86oMyp4z8gSnFb0HkD8dWecMAJlzAxnq62LVz75oM2QhXr3OwHY/f3w3uZdcD6uUFfsAqbCkpCT8+++/AICff/5ZLPgp4eDggB9//BEAsHv3bmRmZpZZXrNmTbHgR5SJiQl+/vlnAMCVK1fw8qV422BR33zzTZngpyoy1NcVfpanWVtmtmAdRWeBNjAQSaec5iaZIq+85Xkl/yav5nUxYZA3AGDJhhMKd3BWBmXKRY7mOZlZ8jdzkppOOeWfmSW9CUNJuvI08ypp0lCRslUGLBvlJXqM5Sqb7Ir9vYqkU5LGm+kkvUrH/BWHAADfTOgOm3L6LFVl6Zmix6D8G2f94iA+PVuxZlaWpvpY+VV36GprIjTiBbp8sQ3H/MOQkp6N/IJCRMa/xp+7r2PovH0oLCxCfUdL/PZZJ7H9jO/pIQx+ft16BZ8vP4Ggp4nIyStAZk4eboTEYPj8/cI3P9MGeKKzp+QBF5SdQYXPGcWaJyqSTtlzRvFmkMaGeujd0V2wr6xc3H8YVc4Wyo0BkAo7efIksrMF7eEHDZI9PKiXlxcAQb+eO3fuyFw3IyMDERERCA4OxoMHD/DgwYMyo9Dduyd7Hprhw4fLk32poqKiZP7cvHnzrfYvD10dLVQzFQwTXV6HwZTUTGFwUlOODtWiaoh0rowtZwCFMgMmlNPJXJqu7QRNKgsLi95qPqHKIigXQwDld9BOSc0U3rhKmytGGtHjW265iOTjzXRK9lPePgT7eSVxH1UFy0Z56epowVxYn72Wua5ofaZoPSPaiTuu3LIp7cRdQ+S47vDzx6vXGTAx0oOZiQEOng4Q+wkIiRSuHxASKfx9UrLsgQSUTU5eAZJSBA8ka1jIHtjAxFAHhiUDh7xQ7O/s711fuO2yXf7IlNJM61JgJC4GCo5tr9YuMDEsGwCP6NIYAJCakYPle6RPYLtg0yXh55FdqubDUF0dLeFUEeXeA6SJnDNS5vKTRnSAhfLOmdgygyZVrC4SHYEzPkl2XaDsqu67K3prt2/fFn6Wt38NAMTHiw87m5SUhGXLlmHfvn0ICwuTOVSirMENDA0NUavW2z3xsbVVbI6D98XZ0QYvA58iPPoF8vMLpA6DKTqMpbNI5SIPF6fS9csbPrNkuaaGOpxsLRVKp0Q1M0Ph5+j4qjk/g4uTNfzvppdbLqLDK7soWC71nESHaU5ADxnrlgwvrKmhjlp2ZculrlN1BIZGITU9CwlJqVKHW45Peo204gExXJyq7lC/LBvl5exggxsp5ddnT8rUZ4r9vaJlWX59JhiWXFCfWQh/X9LM53VaFqbN3ypxW1FbDlzFlgOCAYD2rZgGC/OqNUH446iXsDDVh1MNM2ioq0kdCrtkuGwAePxcdiuMN9UVGTb73hPZ5XIvLB7tPRyhoaGOOjXNywyIUDL89qPnL2U2cYxNSkNCcjqszQ3hbKf4kN3KwtnRGjfuPUNETJLMc+apAsO/v0l0KP8nbwzVLy0dTQ11OIqcM4rgMNj0UUhMlG8umDe92QTuzp07qFevHn799Vc8fvy43BMkK0v6yC5v9l2qyjwbCwK5zKxc3Hsk/VWx6MzPno2dFErDvb69sF23rBmkc/PycedBBADBIAcVbbcr+vRX0eZ6yqJlE8GkcxlZuQiU8Qr/akDp8WzRRLGg3L2Bg/AYi+7nTbl5+bgVFCG2TWleS9O9GhAmI6+ly1o0rppNRgCWjTLzbCKomzKzcnFfzvqseSPF/l430fosUPqksbl5+QgIjhDZRnWf5V4PFgz6Y6inDTdn6Q8D2jQq7Rt7I0T2QEFvyi8oFH7W1JB926ipWbpcdDvR/2tqqKE8WsXBQsEb+6hKSr7/5Z4zIt/15o0UuwdoInLOXA+UXZ+VvPls8hbnjGjTd+tq0uc/qwoYAKmwggLBExhtbW0EBQXJ/dOnTx/hPnJzczFo0CC8fPkSWlpa+PLLL3Hx4kXExcUhOzsbRUVFKCoqwtOnpSe4rABJQ6P8jpxVRTevxsLPu47ckLhOYWEh/jsuaJJnYqSHNk1dFErD0EAX7ZrVBQBcuvVI6tj+Ry/cEz6F7u5d8SYFfmfvCj+LDpRQlfTwKS2X7X7+EtcpLCzE7mOl5dKumWLlYmSgC6/mgm0u3nwodf4Xv/OBwnLp2V68XLp5NYa6uuBmYYef9CYjO/0E3y91dbUy37uqhmWjvMrUZ0dl1GcnbgEoqc+cFUrD0EAXbYvL8/KtR1KbFx67cF9YNt28yx7TWeO7Ie7aXzJ/lv8wTLj+8h+GCX/f2kOx/CqDo9dKA+zhnRtJXEdNDRjcUTAIUkpaNi7fe65QGpHxpQ++WrnKbmHRujjQKiwswvOEsk2knhfvp76jJYwNpD9Aq+9gAXNjwRDLkQrOJaVMurQrLY89xyU3vS8sLMS+knPGUE/h76Chvq7wvuHK7cdSz5njF0vPma7tKlYPpaZn4fA5wT2Anq42Gtezr9B+lAUDIBVWrZrg1XJubi6qVasGV1dXuX5E39KcO3cOz549AwCsXLkSS5cuhZeXF2xsbKCjU1rBvXpVNScAfBseDR3Q0k3wRHuHnz9uBYWLrbNqx3k8Ln6iMmGQt/CpV4ldR28IJyX941/Jk8JNGSYYVS+/oBDfLvlP7InZy5R0LPznMADBTYmkIaz3n7qD1HTZcy4cOhOALYeuARB0huzSVnxUwaqgaUNHtHIXlMu2Q/64ef+Z2Dortp/Do3BB86fJQ3zEymWH33WYNZ8Gs+bTsHjtUYnpTB/REYCgXL7+fY/Ecpn3P0FnbRMjPYzq01psH9YWxhjYVTAC4tnroTgkEoCWOHgmAGevhwIABnfzlNoUqypg2Sgv9wYOaFH8hm6n33XcllCfrd55XviEePxA8fps99EbqN76c1Rv/TmW/HtcYjpThpbWZ9/LUZ8N61U1h+R/VwIex+FakODtwogujdG8vviDqWkDPFHPQdDkafWh22JvZoZ+4opXJ2bj1YnZmD2ijdj2p24+RWFx07ovh7RG9WqGYusAwOhuTeDhImhOf/thLF6lZZdZfuKG4A2FrrYmFk2UPBqsjpYGFk8pHUDh5A3pbwKVneCcEbwF2nXkOu48ED9n1uy6IGwGP3agl9g5s+fYDdi2mwnbdjOxdIPkc2bykPYABOfMnD/3ip0zySnp+GW1HwBBkDW0V0uxfZy/EYqsHOmDY6RnZmPKT5vwqnhy1yE9WpQZUbMqqtq5p7fi7u4u/Hzq1CmMHj1a4X0EBwcLPw8ZMkTqeqL9jVTJwpn90WvScmTl5GHwzH/w+ahP0KapM7Jz8nDwdAC2FgcUte2thBd+RbVr5oK+n3jg4OkAnLz8AAM/X4mJg31gY2GC0KexWL7pFKLjBQHonCm9YWosPmLf1oNXMeu3Xejm1Qgt3eqgjr0VjA31kJmVgyfPE3HkfCDOXAsBAKipqWHhzP4wK+7gWRUt/upTdB23DFk5eRgwfSW+8O2Mds1ckJ2Th32n7mBzcZ+AOvZWmDq8Y4XS8GpeF/07N8X+U3dw/FIQ+k1bgclDfFDd0gQhT2KxdONJYbnMndZHYrkAwJwpvXDWPwRJr9Ix/oeNuBvyHF3aCYLPk5cfYOX2swAACzND/DClZ4XyqkxYNsprwcz+6D15ObJz8jDki1WYMeoTtPEors/OBGCbSH02eWj7CqXRtpkL+nbywMEzATh55QEGf/4PJgz2hrWFCR4+jcVfm08L39p9P6WX1LJRJd+uPoMTS0dAX1cL+xYNwp+7r+PyvefQ1dbEAJ/68O3uBgAIi36JlftuKbz/sOhkbD8dhJFdGqOmpREurvTF6gO34f8gGmlZubAtHgZ7YIeGAAQ34j9vuii2n5X7b2FEl8awMjPAiC6NUbumGTYeDcTjKMEw2I3rWGFin2aoXxysPYxMwo7TQRU/MEpg/oz+6PvZX8jOycOwL1dj+shOaFV8zhw+G4DthwVvumvZWWLSkIqdM22auqBPR3ccOnsXp648wNAvV2H8QG9YWxjj4dM4/L219Jz5bnJPmEoY7nrltjOY/vNWdPNqDM/GteBQ0wIGetpiE6ECgvP7y7FdxfZR1TAAUmHdunWDlpYW8vLy8Oeff2L48OHQLGdiwDfli8yjkZmZCSMj8Q6khYWFWLt27VvntypqVNcOaxb4Yur8rUjLyMYvq4+IrVPb3grbl0yCociQ1opa/v0wpGdk48y1EFy9E4ard8r2SVBXV8OXY7pgVD/xp3slMjJzsPfEbew9IT1YNTXSx6KvBuDTLvLPy6SMGte1w/pfxmLST5uRlpGNBf/4ia1Tx94Ku5dPgdFblMuKH4cjLSMbp68G4/Ltx7h8+3GZ5erqavh6XFeM6d9W6j5sbcywc9lkjJi1FgkvU/HXltP4a8vpMutYVzPGtiUTq+woY6JYNsqrUV1brFngi2nF9dmvUuqzrUsmvlV9tuyHoUjLyMZZ/xBcDQgT62Olrq6GL3y7YFRf6fWZKgl6mohxvx7Cmm96wdhABz+N8RZbJyz6JQb/uBfpWYoNgV1i1opTMNDRQn+f+rA0NcCPEtIAgPSsXHzx1wlcvS/e5yU5NQsDftiDrT/2g2N1U7RytUMrV8nz9t1/koARP+9HXn7V7QMEAK4utlg1fzRmLNiGtIxsiW+la9lZYvPvE8sM0a+oJd8NRVpGDs5dD8G1gDBck3DOfD66M0b0kX7OpKRmYueR69h5RHqT3hZNamPF3JEwM666D0BLMABSYTVr1sSYMWOwdu1a3Lt3D5MmTcKaNWukBkGJiYk4fPgwxo8fL/yds3Npe9XNmzfjm2++Edvuu+++Q0BAwLv/A6qILu0a4fzWb7FuzwWcuRaC2MQUaGtpwMnWEr06uGHsp17Q11V8TH5Rerra2L50MvadvI3dx24gOCwWqemZsDQ3RosmtTD2Uy+ZnSv/mTcKl249wtWAMIQ8icWL5DS8TEmHlpYGqpkYol7t6ujQsj76d2720Txx7ebVCFd2fo81u87j1JVgxCamQEtLA7VsLdGnkzsmDPJ+J+WyZ/kU/HfiFnYeuYEHYTF4nZYFS3MjtHKrjQmDvISDZcjSzNURV3d9j9U7L+DoxXuIihOMwGdfoxq6ezXGlKE+MDeV3CylKmLZKK/ObV1xdsts/LvnIs76l9ZnjjUt0KuDO8Z82u7ty0ZHG9uWTsL+U7ex++hNhDyJQWp6FizMjdCiSW2MHdAOzRTsLP6xO3HjKdpO2YBJfZuhs2ct1LAwQl5eIZ7FvcKhSw+xzi8AWRWYALVEbl4Bxi0+jI3HAjHsE1c0q18D1asZQUdLA2mZuXgSnYwLdyOw+fg9xCZJH2b7wbNEtJm8AUM/cUW3Vs5o6GQJM0NdFAFISsnE/acJOHT5IfZffCjWVK+q+qSNK05v+gbr/xOcM3EvXkNbUwOOthbo4eOGMQPaQe8dnDNb/piIA6fv4L/jIueMmRE8m9TGmP5t0dRV+jnz49Q+uHLnMQIeROBpVCKSUzKQmp4FPV1tWFsYw72+A/p08oC3Zz2oqZU/iEVVoFb0MY1pR2JKvqhz587FvHnzxJanp6ejVatWePBAMPFYgwYNMHHiRDRt2hSGhoZISUlBcHAwzpw5g2PHjqFRo0ZlmrNlZGSgVq1aSExMhKamJiZOnIjevXvDwsICT548wbp163D27Fm0adMGV68Kmq5s3LgRvr6+ZfLh6+uLzZs3w8HBAREREe/lWJSIjo6GnZ3gqVNIWCRqKsmw2VRKW5PdE4kUkS3HbPNUOar3WlLZWSAJog9+VdlZoDfExkSjcT1BoBYVFfVepzXhGyAVZ2hoiIsXL2L48OE4ceIEQkJCMHPmTKnrGxuX7cRrYGCALVu2oG/fvsjOzsY///yDf/75p8w6Pj4+WLFiBVxdq2aneSIiIiL6ePAxK8Hc3BzHjx/H2bNnMWbMGDg7O8PQ0BCampowNzdH8+bNMXXqVBw7dgynT58W275Lly64ffs2RowYgRo1akBLSwuWlpbw9vbG2rVrcfbsWRgYVP32okRERERU9bEJHKkcNoFTfmwCR6QYNoFTXmwCp5zYBE75fMgmcLzLICIiIiIilcEAiIiIiIiIVAYDICIiIiIiUhkMgIiIiIiISGUwACIiIiIiIpXBAIiIiIiIiFQGAyAiIiIiIlIZDICIiIiIiEhlMAAiIiIiIiKVwQCIiIiIiIhUBgMgIiIiIiJSGQyAiIiIiIhIZTAAIiIiIiIilcEAiIiIiIiIVAYDICIiIiIiUhkMgIiIiIiISGUwACIiIiIiIpXBAIiIiIiIiFQGAyAiIiIiIlIZDICIiIiIiEhlMAAiIiIiIiKVwQCIiIiIiIhUBgMgIiIiIiJSGQyAiIiIiIhIZTAAIiIiIiIilcEAiIiIiIiIVAYDICIiIiIiUhkMgIiIiIiISGUwACIiIiIiIpXBAIiIiIiIiFQGAyAiIiIiIlIZDICIiIiIiEhlMAAiIiIiIiKVwQCIiIiIiIhUBgMgIiIiIiJSGQyAiIiIiIhIZTAAIiIiIiIilcEAiIiIiIiIVIZmZWeAqDLlFxYiv6CwsrNBb8jMya/sLJAEpgbalZ0FkkJXS6Oys0BS3N06rbKzQBJ4/HC8srNAb8hPS/pgafENEBERERERqQwGQEREREREpDIYABERERERkcpgAERERERERCqDARAREREREakMBkBERERERKQyGAAREREREZHKYABEREREREQqgwEQERERERGpDAZARERERESkMhgAERERERGRymAAREREREREKoMBEBERERERqQwGQEREREREpDIYABERERERkcpgAERERERERCqDARAREREREakMBkBERERERKQyGAAREREREZHKYABEREREREQqgwEQERERERGpDAZARERERESkMhgAERERERGRymAAREREREREKoMBEBERERERqQwGQEREREREpDIYABERERERkcpgAERERERERCqDARAREREREakMBkBERERERKQyGAAREREREZHKYABEREREREQqgwEQERERERGpDAZARERERESkMhgAERERERGRymAAREREREREKoMBEBERERERqQwGQEREREREpDI0KzsDRKogJv4V1u+9hLP+wYhJSIGOliYcbS3Qq70bRvdvCz1d7XeSzqEzAdh97AZCn8bhdVomLM2N4NmkNnz7tUVTV0eZ27b4dD6i41+Vm4atjRlu7J37TvJb2WISXmHTvss4fz0EsYkp0NbSgENNC/TwccPIvm3eWbn4nb2L/07cxMOnsXidlgVLcyM0b1wLo/q2gXtDx3K3LyoqwrGL93DodAAePI7Gy5R0qKurwdLcCE3q2ePTbp7w9qz3TvKqLKLik7F21wWcuhqM6PhX0NbWRC1bS/Tt5I5xA72g/47KZt+p29jhdx3BYbFIScuEVTVjtHKrjfEDvdC8kZNc+0hOScea3Rdx9OJ9PI99CQCwr1ENPbwbY9Jgb5ibGr6TvCoLlo1yikt8hZ2Hr+LKrYeIS0yBtpYm7GpUQ+d2jTGwR6u3qs/yCwrwJCIeDx5FIfhxNIIfR+HZ80QUFBYCAI5unI0a1ubl7qe772LEJZZ/naluZYZjm76tcH6VTXVTPYxs5wSf+laobqqH3IJCPE/KwPF7sdhxNRLZeQUV2q9n7WrY+llrhba58SQJo1b5l/mdloY6vOtbobG9KRrZmaK6mR7MDLRhoKOJ9Ox8hL9Ih//jJOy+HomE19kVyquyUSsqKiqq7EwQfUjR0dGws7MDANx/FI6aNW3fa3pnrgVj+s9bkZouudKobW+FrX9MhENNiwqnkZ2Th0k/bsKZa8ESl6urq+HLsV3xhW8XqftQpgAoN7/wve4fAM75h2Dmwm1Iy5BcLrXsrLDxtwmwr1Gtwmnk5OThs3mbcc4/ROJydXU1fO7bBTNGdZa6j9T0LEyaswHXA5/KTKtnezcs/X4YtLXe33MtU4N3c2NbnpNXHmDij5uRmp4lcbmzgzX2LJ8CR9u3O2d8v12Pk1ceSFyurq6Gbyd0x9fju8ncT0BwJIbPWoP4pFSJy6tbmmD7kolwb+BQ4bwqE5aN4iJeZLzX/QPA5Zuh+P6PXUiXUp852lrif/PHwLZ6xeqzNdtPY/X2M1KXV8UAqMvic+91/yW861thyXAPGOtpSVz+LDEdE9bdQHRypsL7rkgAtMs/EnP33i/zO/tq+jj9fcdyt83Iycf8fUE4dCdaoTTllZ+WhNiNYwEAUVFRsLV9f/dnfANEEvn6+mLz5s1wcHBAREREZWenygoOi8HknzYjKzsXBno6mDayE1p71EF2Th4On7mL7X7+ePo8EaO+WYtj676Cgb5OhdKZtXiXMPhp7eGM8QO9YG1hgodPY/G/rWcQEZOEJf8eh3U1Ywzr1Urmvrq0c8U3E3pIXa6lqVGhPCqTkCcxmDZ/i7BcpgzviFbugnLxO3cXu45cx7OoRIz9dh0Orf6iwuUy+4/dwuCnlXsd+A7wgrWFMR49i8M/284gMvYl/txwAlbmxhjSs6XEfXy+YKsw+LGrbo6JQ9qjrlN15BcUIOhRNNbsPIfk1xk4cj4QpiYGWDBzQMUOipIIehyNsd9tQGZ2Lgz1dTDTtzPaNXVGdk4e9p+6g80HryEsMgGDv1iFs5u/gWEFy2b6gu3CG+x2zVwweYgPbCxMEPI0Fss2nkR4dBJ+WXMU1hYmGNVX8g1GbGIKhn65GonJadDUUMdnwzugS1tXAIJA4Z/t5xD34jWGfLEa57fORg0r0wrlVVmwbJTTo2ex+ObX7cjOyYO+njbGDmqPZo1rIycnDycv3cP+EzcREf0CM+ZtxLbl06Gvp3i5iD4q19HWhEutGkh5nYGouJcVyrNPywaYOkr6A7mP4ToDAHWrG2P5yKbQ19FERnY+1pwLw40nL6GrpY7ubjUxuJUDalkZYu14T3y6/DIycxV7ExQUlYKef1wod70f+7miRR3BQ4mDt6MkrpOUloMbT5IQFJWC2FdZeJGajbyCIlib6MKngRV6edjCQEcTi4e44VVGLi49TFQor8qGARDRezT37wPIys6FpoY6dvw5Gc1cS5tttG3qAic7Syz85zCeRCZi7e7z+GJMV4XT8L/7BAdO3wEAfNKmIdb/Mg4aGoLufW717dG5rSu6jluKmIRXWPSPH3r4NIGJkb7U/Rkb6qFereoK56MqWbDioLBctiyZBA+RZmitPZzhaGuBxauP4OnzRKz/7wJmjJZ+oZbmeuATHDoTAADo2Loh1iwYIyyXJvXs0al1Q/Sa9CdiE15h8Ro/dPduAmMjvTL7CHoUhQs3HgIQNNs5+u9XMNTXFS5v5e6MHu3d0H3cEqSmZ2HH4WuY6dsF1apws57vl+1DZnHZ7PvfVHg2riVc5tW8LmrZW2Hu3wfxOCIB/2w/h28myH4LIMnVO2HYe/I2AKBrO1ds+2OisGw8Gjqgm1cj+Iz8DdHxrzD3fwfRp6ObxHNm4T9+SExOAwCsW+iLvp08hMtau9eBe317jPluAxKT0/DL6iNY8dMIhfOqTFg2ymnJWj9k5+RBU0Md/ywcjyb1S99oebrVgX0NCyzfcAzhUS+w9cBlTBrWSeE0Gte3xw/T+qGhix2cnWygqaGBn5btqXAAZGSohzqONhXatir5oW9D6OtoIq+gEGPXXkdgZOnbr+tPXiIyKQPf9GqA2tZGGONdCytPhym0/6zcAoTFp8lcx0hXE24OZgCAiBfpuBsh/gYuKjkTbeadkrh9UBRw5kE8dvs/x45pbaCtqY7Pu9at8gEQB0Egek8CQ5/D/+4TAMCQni3LBD8lJg3xgbOjNQDg3z2XkJeveDvgf3YIXuNraKjjl68GCm8WSpibGuKHKb0AAClpmdh55IbCaXxM7j18LnyjMqh7izLBT4kJg3xQx0FQLhv2Vqxc1u46DwDQUFfHgpkDJJbLtxN7AgBep2Vh9zHxcrn9IFz4ecynXmWCnxI1rc0wsJsnAKCwsAiBIZEK51VZBARH4sodwQ3AiD6tytxgl5g2vAPqOglunFbtOl+hsvlrq6Apj4aGOpbMHixWNtVMDTFveh8AQEpqJrYe8hfbR+LLVOw5cQsA0LFl/TI32CX6dvJAx5b1AQC7jt1E4kvJTbGqApaNcgp+HIXb958BAPp0bl4m+Ckxsn87ONlZAQB2HLxSoXJp3bQuPu3eEvXr1ISmxsfxduZ9a2RnInzrsu/G8zLBT4kNF5/iSXEAM8qrFjTV1d55Prq71YSOlqDMDktpuiZPZ5igqBRcD0sCALjamUJfu2p/DxgAkUSbNm1CUVERm7+9hROXgoSfB3f3lLiOuro6Pu3SHIAgOCkJmOSVkZmDq3ceAwC8mrlIbcbRzbsxjAwEN8/HL92XuI6qOCXSr+DTbtLLpX/nZgAEwcn1wIqUi+BmsW0zF1SXUi5dvBoJy+XkZfFyyRPpGGsvo+2+aD+l3Lx8hfKqTI5evCf8PFxKU011dXXh+ZSSmokrxd9/eaVn5uDSrUcAAB/PeqhpbSZxvV7t3YRl43f+ntjy45eCUFAg6Ks2rJfk5osAMLRXCwBAQUEhjovUCVUNy0Y5nfcv7ffZ55NmEtdRV1dHz46CIDA1PQt37svuT0jvRkfX0pYU+25JbnZWVAQcLA5KTPW14Vmn4n1OpenbTNCPprCw6K377mTmll5ftDWrdghRtXNPpMRuFl9k9PW00biundT1WrrXFtnmmUJp3A2NRE5xhdTSrY7U9bS1NOHRUPBkMDAkskJPAD8Wt4qPsb6uNhq5SO9g2cKttFxuB4VLXU+Sew+fCwORFk1qS11PW0sTbsUdsO+FPhcrFyc7S+Hn5zKampSMbvXmNlWNf/GbOQM9bbjVk37OtPEo/a5fD1TsnAkIjhCeM6L7eZO2liaaN3IUbvNm2fiLDErRxsNZRl5Ll12/V3VvPFk2yingQQQAQE9XG/Wda0pdr2mj0jd2d0Mi3nOuCACaOQkGhcjIyUdw9Gup6916Wlp/N3UqfyAJRdhV04dH8T5vhycjOlny4CXyMDfURktnwRut5PQcpGTmvZM8Vhb2ASJ6T8IiEwAAjjUtoCmjQ2cde+vSbSISFErjicj6dRysZK5bx94aF28+Qn5BIcKjXsDFSXL76xuBz9Bp9G+IiHmJoqIiWJobwa2+Pfp28kCXdo2gpvbuX9F/SE+Ly8WhnHKpbV96PJ9EKlguIuuL7kdaOpdvCcolIvoFnEXaxXs1rwdbG3NExydj095LGNTNU6wDc1xiCvaeuAkA8GjoiHq1aiiUV2XyOFxw3JxsLWWWjYvIMXocEa9QGo/CS9d3cbSWsSbg7GCDc9cfIr+gEE+fJ5bpG/e4eD/GhnqwtjCWug8bCxMYGegiLSNb+PdVRSwb5RQeJeiHYVejmsymaU62pQ9Gwp9Xft+NgAfhGPTZn8I+ROamhnB1sUNXbzf4tGpQ5a8zAFDbWtAX83lSBgoKpbcxe5aYXrqNldE7zUPJ2x9A+uAHsmhpqMPaRAetnC0xoUMdmOoLRgLdclmxh4LKSKXfAMXGxuLbb7+Fh4cHTExMoK2tDRsbGzRq1AhDhw7Fpk2bkJpa2i44IiICampqUFNTw6ZNm2Tu29HREWpqavD19RVbtmnTJuF+IiIikJOTg+XLl6Nly5awsLCAmpoa5s2bJ3Vft27dwtChQ2FnZwddXV3Y2dnB19cXoaGhUvNz4cIFYZoXLlxAYWEhNmzYgPbt28Pa2hrq6upl8urr6ws1NTU4OjpK3eeBAwfQt29f2NraQkdHB0ZGRqhVqxbatWuHH3/8ETdv3pR5jG7evIkJEybAxcUFhoaGMDAwQL169TB16lSEhSnWEVDZZOfkITlFMPSptOZPJUyN9aGvJ6hUYuUYHlRUbGKK8HN56Yg2j5OVzvO4lwh9Goes7Fxk5+QhKi4ZfucCMe77Dej32d+Ie5EidVtll5OTh+TXxeViaSpzXRMjfeF8JnEix1keouvblJOOaD5i30hHR1sTy+cMh6mxPiJjX6L7uCXYcfgabgeF4/rdJ1i3+wJ6TVqG12lZsLUxx++zhyiUT2WSnZOHlymCG4Ea1qYy1zU11odB8TkTk6DYOROTkCL8XN7IXzVF8vFmOiX/l2f0sJKmXIrmVVmwbJRTTm4eUlIF9Zl1NROZ6xob6QvnAYpPkv424kOJiU9GWEQ8snPykJ2Th9iEVzh1+T6+XLgFY79ejUQlyOPb0NZUh7mh4GFVfDnz5qRm5SEjR/Dm08ZUvJ/n2+jtIQiAsnILcOJenFzbeNauhkdLe+HR0l548HsPnP2hExYOagIHCwMAgn5E/56vmm9MRansG6DLly+jZ8+eZQIcAEhISEBCQgIePHiAXbt2wcLCAj179nxv+UhKSkK/fv0QGBgo1/obNmzApEmTkJ9f2g4zOjoamzdvxq5du7B582YMHjxY5j6ys7PRpUsXnDkjfUx/WQoKCjB06FD8999/ZX6fm5uL9PR0hIeH48qVKzh+/Dhu374ttn1+fj5mzJiBVatWiS179OgRHj16hHXr1mHlypWYMGFChfJY2TIyc4SfDeQYclRfVxuZWbnIzMpVKJ10BdLR0yudwyVDQjpampro3NYV3s3rom6t6jAy1EVqWhbuBEdgy4GriE1Mwa2gcAz9YhUOr54JY0M9sX0ou/Ss0uOlr1f+nDZ6etrIzM5Fpsh2iqZjUE46ovmQlE5TVyccWfcVNu27jM37L+OHZXvLLDfQ08EXY7pgRJ82VXpSx/TM0psEuc4ZPR1kZOWWOQcUTqecYZpF37ZlvFE2JenKM9RzyXfgzX1UFSwb5SSaZz156jNdbWRl5yJLwevMu6SlqQHvlg3Qyt0ZtR1tYKSvi7SMLNwLfY69x/wR/+I1AkMiMPmHf7F52WcwMqh61xkAMNApvb3OzCm/X2ZWbgEMdDShr/PubsubOpnDvjhoOfMgThhkVVT0y0zM3XcfVx69eBfZq3QqGQDl5ORgyJAhSE1NhZGREaZMmYL27dvDysoKeXl5iIyMhL+/P/bt2/fe8zJu3DgEBQVh1KhRGDx4MGxsbPD8+XPo6IhX3IGBgdixYwesrKzw3XffwdPTE9nZ2Th27BiWL1+OnJwcjBgxAk5OTvD0lNy5GwBmz56N+/fvo3fv3vD19YWDgwMSEhLEgkFpVq1aJQx+2rZti/Hjx6N27dowNDREcnIyHjx4gOPHjyM5OVnq37xlyxYAQLdu3TB8+HC4uLhATU0NgYGBWL58OYKDgzFx4kTY2NigV69ecuVLmWTnlraNlWc+g5LJK7NzFGtTm6NAOjoiE2RKSufoui8kDifb2sMZYwa0w8Q5G3Hx5iOERSRg2cYTmDe9n0J5VQZljpccE4bqVLBcchVIR7uccikqKsLxi/dw/OI9iX23MrJy4HcuEDWtzTGga3OF8qlMskUuzvJM5lrxc0a0E6/sdHS0S5dnZZdNp+Qc19KS4/wu3k+WgnlVFiwb5ZQrcry0yjleAKBdfDxE68EPbdvyaTCS8PCsWePaGNKrNb7+ZSv8A8IQHpWINdvPYNbEqnf9BwAdrdIGVnkF5U/sXTL5t64c31l59Wkq2vxN/sEPROcW0tZUR3VTPXRoaI0+TW3x62A3/HXiIfbeVLw5nbJRyQDo6tWriI2NBQDs2LFD7A1PixYtMGjQIPzxxx/IzFR8Zl5F3L9/H+vXr8fYsWOFv/PwEB+yEwDu3bsHBwcHXL9+HTY2Iv0EvLzQpUsXdO7cGfn5+Zg6dSpu3bolM80ff/wRP//8c4XyvGfPHgCC43T+/HlovlHxdujQATNmzJAYAO3bt08Y/Kxbtw7jx48vs7xZs2YYMWIEevTogXPnzmHGjBno1q2bWBqyREfLPtHj4uR7Dfw2dLVLZ3yWZ8CBkg7zujqSZ4qWRkeBdHJERgeTlI6suYEM9XWx+mdftB68EK9eZ2D7YX98P7mXXDdDyqTM8ZJjtLScCpaLtgLp5Mool8LCQkz/eSuOXRCMdDWoewuM6tcGdeytUVBYiJAnsViz6xzOXA3GrMU7EfosFnM+66NQXpWFrsiTT3lGsqv4OSOSTr7sdERvyPV0y6ajq62FzOzcMiP1SVNyo6qnYF6VBctGOWmLHK+8co4XAOQWHw/RevBDkxT8lDDQ18Fv3w1H73G/IyU1E/tP3MTnY7rJ9bBK2eTklQY9Whrl9zYpGVEtW47vrDy0NNTRtYmgX1zC6yxceyz/W5s35xYKjn6NMw/icTggBmvHeWLRYDdYm+gqPGeRslHJPkDx8aUdLb28vKSup6mpCWNj6R0o34UOHTqUCX7Ks3Tp0jLBT4n27dsLm4vdvn1bZgDk4uKCuXPnKp7ZYiXHr3Xr1jIDE3Nz8dFMfv31VwBAv379xIKfErq6ulixYgUAQb+rCxcuKJQ/Ozs7mT+y3o69K6LNN+RpWpGZLWiSIE+zLFGGCqQj2uyhvGZZkhgb6qFPR3cAQGZWLu49rHpPgAxFms3I09yw5JgpOnO6YZnmObLTEc3Hm+lsPXhVGPzM9O2C374ZjIbOttDR0YK+ng6aNXLCukXj0K94yO71ey7inH+IQnlVFqJzHMl1zmTJ38xJajrlNNHKzJLexLQkXXmaeZV8B+RpPqaMWDbKSTTP8jRryyq+zsjTXK6yGBnooYtXEwCC/AaHvd2wzZVFtLmZPM3a9Irn1JGnuZw8Orlaw6R4wAK/gBjIGINBbtfDkoSDH0ztXBe1rKpuk2tARQOg6tVLR4vZuHFjJeYEGD58uNzrmpmZoU8f6U93RQMpWf17Bg8eDI23mMis5Pj5+fkhKSlJ7u1iYmJw584dAMCgQYNkrlu/fn1YWAiGW/T3F5/oTtnp6mjB3FTQ9ra8DvQpqZnCm+AaVpLnvZBGdOCD8tIR7WCvaDolREcoi39R9Tqp6uhowdykuFzKGczhdVqmMDAtb4CJN9lYlnZIji8nHdF8vNlpe88xwUAihvo6mDyso9R9fD2+u/Dz7qPX5c+oEtHV0UK14j5MsSKd4SVJSc0U3rhKmytGGtFO/G8OOvEm0U75b6ZTsp/y9iHYzyuJ+6gqWDbKSUdbC6bGgvos4aXs+jg1LVMYANlYyB4wobLVEhkZ9UUVnaA2N78QyemCANzGRPbABsZ6WsI+Q/EpsgdMkFefZqVD1SvS/K08Z4MFD8A11NXwSSPJI8lWFSoZALVt2xa1agnGxJ85cyY8PT3x66+/4tq1a8jN/bCdAxs3biz3uu7u7jLfuLi5uUFbWxDxP3jwQOp6iqQpyejRowEAT548QZ06dTB27Fjs3Lmz3KZnogMiDB06VDgqnbSfkuBK9I2dPKKiomT+lDc63bvi7CCoxCNikpAvo3nak+elw686lzP0q1gaIus/iZQ9tGlJOpoa6nC0tVAoHSF5potWcrWLyyWynHJ5KjJUbB0HRcul9MLwtJwhZ0uWa2qow6Fm2XIpGU67joN1meZBb6puZQoLMyO50lNmLk6C4xwe/UJm2YgOryw6N/HIZAAARNtJREFU7LI86jmJDtMse+jjsEhBOpoa6qj1xvxKdZ0ED4JS07OQkCT9Ji0+6TXSMgQ3NSV/X1XEslFOTnaCYfajYl8iv0B6uYRHlzaBcipnaP7KVvQRXGcA4GmCYOREewsDaKhLH9Zb9E3K08Q0qevJy9xQG23rCs6JB1EpZZqzva3k9NJ75Bpm0pvNVwUqGQBpaWnBz88P9evXByAYVvr7779HmzZtYGpqim7dumHHjh0okFGZvCtmZvI/dbKykl1paWpqCpudSRuAQNE0JRk7diy+//57aGpq4vXr19i4cSOGDRsGOzs71KlTB7NmzcKzZ+IT4CUmVuzGTNF+WLa2tjJ/RN8Avk/NGwuC7MysXNx/JL252PW7pcNJNm/kpFAabvXthR1brwc+kbpebl4+AoIjAQBN6ttXuO+O6E2JjYz5NZRZyTHOzM5F0GPpQfsNkckUm7oqVi6N69kJy+WGjAkWc/PyERgSWbyNeLloFrcdL5CjE23JzY+mHO3NlVXL4kljM7JyESijieXVgNLveosmtaSuJ4l7AwfhcRbdz5ty8/JxKyhCbJvSvJamezVAelt40WUtGiuWV2XCslFO7g0dAQiai4WGxUhd705Q6TXZrYHje87V23km8hDH0rxqXmcA4E644D7MQEcTDW2lv3VrXrua8HNA+NsPx97Lvaaw39GhO++2CaG1yNusd9Vcr7JU3SvlW2rQoAGCgoJw4MABjB07FrVrCyr3rKwsnDhxAsOHD0eLFi0qfNMuL0WaoskzMZg8T07epvlbiUWLFuHJkydYtGgROnToAH19wZOAp0+fYunSpahXrx5Wr15dZhvRgHL79u0ICgqS62fhwoVvnd/K0LVdI+Hn3cckv3UqLCzE3pOC/lomhnpoLWPWckkM9XXRtqkLAODy7cdSm3wcv3hf+KSzm1fF3gCmpmfh8Lm7AATDqTauZ1+h/VS2T9q6Cj/vPS69XPafEryxNDbUQyt36bPSS2Koryssy6t3HkttnnjyUpCwXDq3cxVbbldd8EDjUXg8UtOkz+D96FkcUlIFDwpsq1eTup6y6+FT+t3c7ie56WthYaHwfDIx0kO7Zi4KpWFkoAuv5oJtLt58KHX+F7/zgcKy6dm+idjybl6NoV78VHeHn/Rmhzv9bgAA1NXVKnzuKQOWjXJq36qh8POh0+LTTgCCcjlyNgCAYBCCZo1rf5C8VURaRhZOXRb0e9TV0UIDF9tytlBeZx6Uvg0d0NxO4jpqakDf4tHaXmfm4sYT+bsVSFPS/C2voBB+AdKD4oooGVgBAB7HV83miSVUNgACBIFA3759sX79ejx58gSxsbFYv349mjZtCgC4c+cOJk2aJFxfXb30cBUWyn4im5GR8c7zm5Agu0lAfn4+Xr0SXDAkDUDwrjk4OOD777/H2bNnkZKSgitXruDzzz+Hrq4u8vLy8Nlnn+Hu3bvC9atVK70xU1NTg6urq1w/NWvWfO9/y/vg3sBB+AR015HruP1AfObkNbsuIKz4rcq4gV5iQ1nvPnYDNdvORM22M7F0/XGJ6Uwa2h4AkF9QiB+W7RV7W5Ccko5Fq/wACIKsoT1biu3j/PVQZOVIb/6ZnpmNyT9twqviSUSH9mwhs0mWMnOr7yB8O7fn2A0EBEeIrbNuzwVh87MxA9qJlcve4zfh5PMlnHy+xPKNJySmM2Fwabn89Nc+ieWyeO0RAIIga0gP8XLp2Fpwc5Obl4+F/xyS+IAjJycP8/93QPj/Dq0aSMxPVdC0oSNauQtuzrYd8sfN++JvkldsP4dH4YIbi8lDfMTKZoffdZg1nwaz5tOweO1RielMHyHoT5VfUIivf98jVjYvU9Ix73+HAAhu5Ef1aS22D2sLYwwsHnb87PVQHDp7V2ydg2cCcPa6YILqwd08YV1F35oCLBtl5VrXDu4NBW+oD526hXuhkWLrbN1/GeFRgoe5w3q3ESuXw6dvw737bLh3n43V206/t7xevf1I5tDoGZk5mP3rduHDnL5dmle5kUZFBUWl4NbTlwCAAS3s4eYg3vpmrHdt1LERNF/ecjkc+W+MVtCvua1wUtJpnct/oFDH2lD4tunyw0S8ypCvW0cP9xow1JV9rLs1qY7BLR0ACCZvPfdA9j2psqu636z3oHr16hg7dixGjhyJli1bIiAgAEeOHEFWVhb09PRgZGQkXLck0JDk5cuXCg0OIK/AwEDk5+dL7Qd07949YR8mV1fxp8nvk5aWFtq0aYM2bdrA29sb/fv3R1FREfbu3Qt3d8HIYSX/AsCpU6cwdOjQD5rHyvDz5/3RZ8pfyM7Jw7AvVmP6qE5o7e6M7Nw8HDoTgO2HBU9Sa9lZCgMZRbVt6oI+Hd1x6OxdnLryAEO+WIUJg7xhbWGMh0/j8PeW08Inqd9N7glTY/F2uyu3ncG0n7eiu3djNG9cC441LKCvry02ESoA1La3wpdju1bsgCiJudP74tNp/0N2Th5GzVqDz0Z0RCu3OsjOzYPfubvYWfzU2MnOEuMH+1QojdYezujVwR1+5+7izNVgjJy1GmM+9YJ1NRM8Co/Dyq2nEVtcLrMn9pA4DPm4gd7YfewGXr5Kx3/HbyI8+gWG926N2vZWgmGww2Kwaf9lYRBdx8Ean1bhuYAAYPFXn6LruGXIysnDgOkr8YVvZ7Rr5oLsnDzsO3UHmw9cBQDUsbfC1OHSB4aQxat5XfTv3BT7T93B8UtB6DdtBSYP8UF1SxOEPInF0o0nER0vKJu50/pIPGcAYM6UXjjrH4KkV+kY/8NG3A15ji7Fb/JOXn6AldvPAgAszAzxw5T3N6H2h8KyUU7fTO6FMbNWITsnD5/N+RdjB3VA8ya1kJOTj5OXArGv+E23Q00LjOwvfeRbWTKzcnDmSlCZ30XFvhR+PnMlSDggAwDUrVUDdWvXKLP+xv8u4Ic/dqFD64Zwa+gEu+rm0NfVEZsIFQAcbS0xefgnFcqrMll06AF2TmsLPW0NbJjYEqvPhuHGkyToammgu3tNDGklCCjCE9Ox8aL05tLy6ifypunALfmbvw1u6YAFnzbBmQfxuPXsJcJfpCMtKx/6OhpwsjREl8bV4dNA0E+usLAIiw4+wOusqjd3ligGQBJoaWnB29sbAQEByM/PR0pKCvT09GBmZgZTU1OkpKSU6dD/pp07d76XfCUnJ8PPzw/9+kmegHLDhg3Cz506dXoveZBHx46lFz7RQLBOnTpo0KABQkJCsGvXLsyfPx/29lWzGZW8XF1ssWr+aMxYsA1pGdlYvEb8qWctO0ts+WNimSFgFbX0+6FIy8zBOf8QXAsIw7U32r2rq6th5ujOGNm3jdR9pKRmYoffdZlNRlq61caKuSNhJnKhq4oaOtvif3NH4ctF25GWkY0/1h0TW8fJzhIbfh3/VuXy++zBSM/MxvnrofC/+wT+d8v2a1BXV8P0kZ9gWG/xp9gAYG5qiC2/T8LknzYiKi4Zt4PCcTtI/E0iADSoUxNrFo6p0k9MAaBxXTus/2UsJv20GWkZ2Vjwj5/YOnXsrbB7+RQYGVS8bFb8OBxpGdk4fTUYl28/xuXbj8ssV1dXw9fjumJM/7ZS92FrY4adyyZjxKy1SHiZir+2nMZfW8o+QbeuZoxtSyZWyVHG3sSyUU71atfE4m+HYc4fu5CemYMVm8XfSjvUtMDf88eUmaJBESmpGZj7539Sl/+5vmwdOmlYJ7EACBCMrnng5C0cOCl9qg4PVyf8+s1QmXPTVRWhMan4Yusd/DHMHUZ6WviqR32xdcIT0zHx3xvIyHm7fudqakBPd0GLmZTMXJwPUewNjYGuJvo0s0WfZtKbHaZk5mLhgQfvvGldZajaV8oKunz5MqpXr446dSS368/NzcXFixcBAIaGhrC0LB1hxsvLC4cPH8ahQ4fw9OlTYd+hEqGhofjpp5/eW96//PJLtG7dGtbWZUesuXjxItauXQsAaNq0KZo3f39Pgbdt24YhQ4ZIfRN16tQp4Wcnp7Kdx+fMmYNhw4YhOzsb/fv3x/Hjx8scX1E5OTnCSWJ1dSt+Ma1sndu64szmb/Dvfxdx9loI4l68hramBhxtLdCzvRvGDGgHPd23m5dBT0cbW/+YiAOn7mDP8ZsIeRKD1PQsWJgZoUWT2vAd0BbNZHTk/3FaH1y5/Rh3giPw9Hkikl9nIDUtC3q62rC2MIZ7Awf07eQBb896cvVFqwo6tW6IY+tnYdO+yzh3PQTxL15DS1MDjjUt0M2nCUb3a/vW5aKro40Niyfg0Jk72HviFkKfxiKtuFyaN66FUf3awqO4E7M0DZxr4sSGr7Hv5G2cvvoAD5/G4nVaJgA1WJgZooFzTXT3boKeHdzFmrZUVd28GuHKzu+xZtd5nLoSjNjEFGhpaaCWrSX6dHLHhEHe0H/bc0ZXG3uWT8F/J25h55EbeBAWg9dpWbA0N0Irt9qYMMgLnnJ0jG/m6oiru77H6p0XcPTiPUTFCTo+29eohu5ejTFlqA/MTav2fBmiWDbKybtFA+xe+QV2HLqCK7ceIiHpNbS0NGFXvRo+adsIg3u1fuv67G19Ma4Hbt57gvuhkYiMeYFXrzORnpEFXR0tWFYzhmtde3T1boJWHi4fzXUGAM6HJKD30osY1c4JPvWtYW2ii7yCQjxPysSJ+7HYdiXinUyA2srZAjamgolmjwfGIk+OwXNKzNp+F61dLNCijgXqVjeGhZE2zAx0kFdQiFcZuXgcl4rLj17gSEAMUqv4m58SakUfy3iDCpg3bx4WLFiAdu3aoUePHmjcuDEsLS2RlZWFx48fY/Xq1cKhkmfOnIk///xTuO3JkyfRtaug+Y+trS3mzZsHd3d3pKen48yZM/jrr79gZWWF169f48WLFxg9ejQ2bdpUJv1NmzZhzJgxAIDw8HA4OjrKzK+joyMiIyPRpEkThISEwMrKCt999x08PT2Rk5ODY8eO4c8//0R2djY0NTVx5coVtGjRosw+Lly4gPbtBU2szp8/Dx8fH5lp+vr6YvPmzXBwcEBERESZZWpqarC2tkb//v3RunVr1K5dG7q6ukhISMDp06exatUqZGVlwdDQEKGhobC1tZW4bwCwsLDApEmT4O3tDUtLS2RkZODp06e4fPky9u/fj+TkZKSlpcHQ8N1dpKKjo2FnJ3hNfP9ROGrWrLqdLD9WufnyV9z04ZgaKO8EikTKKuLFu+8TTG+vy+JzlZ0FekN+WhJiNwrmtIyKihK7f3yXVPINECAYxODixYvCNz2S9O/fH7/++muZ33Xp0gUzZszA33//jejoaIwfP77Mcjs7Oxw6dAjdu3fHu+bm5oZp06ZhypQpmDZtmthybW1tbN68WSz4eR8SEhKwatUqrFq1SuJyU1NT7N69W+KXd/369bC2tsbSpUuRlJSERYsWYdGiRRL3Y2Bg8E5GrSMiIiIiAlQ0APrmm2/QokULnD59Gv7+/oiNjRUOd21jY4MWLVpg1KhRUoOYv/76Cy1btsTq1asRGBiIvLw82Nvbo1+/fpg1a1aZ0c7etfHjx8PV1RV//vknrly5gqSkJFhaWqJjx46YPXs2GjR4/yNAPXz4EKdPn8bZs2fx+PFjJCQk4PXr1zAyMkLdunXRtWtXTJkyReq8RRoaGvjtt98wbtw4rF27FufOnUNERARSU1Ohr68Pe3t7uLm5oXPnzujXrx/09PTe+99ERERERKpBJZvAVTUlTeAkNacjxbEJnPJjEzjlxCZwRIpjEzjlxCZwyudDNoFT6XmAiIiIiIhItTAAIiIiIiIilcEAiIiIiIiIVAYDICIiIiIiUhkMgIiIiIiISGWo5DDYVc2bE5ESEREREVHF8A0QERERERGpDAZARERERESkMhgAERERERGRymAAREREREREKoMBEBERERERqQwGQEREREREpDIYABERERERkcpgAERERERERCqDARAREREREakMBkBERERERKQyGAAREREREZHKYABEREREREQqgwEQERERERGpDAZARERERESkMhgAERERERGRymAAREREREREKoMBEBERERERqQwGQEREREREpDIYABERERERkcpgAERERERERCqDARAREREREakMBkBERERERKQyGAAREREREZHKYABEREREREQqgwEQERERERGpDAZARERERESkMhgAERERERGRymAAREREREREKoMBEBERERERqQwGQEREREREpDIYABERERERkcrQrOwMEH1o+fn5ws/x8XGVmBOSJi+/sLKzQBKk62tXdhaIqpz4l5mVnQWSID8tqbKzQG8oyEgWfha9V3sfGACRynnx4oXwc2fv1pWYEyIiIiJ604sXL+Do6Pje9s8mcEREREREpDLUioqKiio7E0QfUnZ2NoKCggAAlpaW0NSs2i9C4+Li4OnpCQC4efMmqlevXsk5IoDlosxYNsqLZaOcWC7K62Mqm/z8fGErnUaNGkFXV/e9pVW17/yIKkBXVxfNmzev7Gy8F9WrV4etrW1lZ4PewHJRXiwb5cWyUU4sF+X1MZTN+2z2JopN4IiIiIiISGUwACIiIiIiIpXBAIiIiIiIiFQGAyAiIiIiIlIZDICIiIiIiEhlMAAiIiIiIiKVwQCIiIiIiIhUBidCJSIiIiIilcE3QEREREREpDIYABERERERkcpgAERERERERCqDARAREREREakMBkBERERERKQyGAAREREREZHKYABEREREREQqgwEQERERERGpDAZARERERESkMhgAESkhR0dHqKmpwdfXt7KzorI2bdoENTU1qKmpISIiorKzo1J47Ikq7vnz55g0aRJq164NXV1d4bl08ODBys4aVRG+vr5QU1ODo6NjZWflvdGs7AwQERER0dt7/vw5mjZtiqSkpMrOCpFSYwBERERE9BFYuHAhkpKSoKmpiUWLFsHLywuGhoYAAAcHh0rOHZHyYABERCSBr68vmyASUZVy5swZAEDfvn3xzTffVHJuqKratGkTNm3aVNnZeK/YB4iIiIjoIxATEwMAcHFxqeScECk3BkBEREREH4Hc3FwAgJaWViXnhEi5MQAilRQbG4tvv/0WHh4eMDExgba2NmxsbNCoUSMMHToUmzZtQmpqapltMjIysHv3bowfPx5ubm4wMTGBlpYWLC0t4e3tjSVLliA9PV2u9I8dO4Zu3brB0tIS+vr6cHFxwZdffonY2Nhyt31zhKzCwkKsXbsWrVu3hpmZGQwMDNC4cWMsWrQImZmZ5e6vqKgIe/fuxYABA2BnZwddXV2YmZnB09MTCxYsQEpKisztHz9+jOnTp8PV1RWGhobQ1tZGjRo14ObmhrFjx2L37t3IyckR266goACbNm1Cly5dYGNjA21tbZiamsLZ2RkdO3bEL7/8gpCQkHLz/77IMxLZixcvMGfOHLi7u8PU1BS6urpwdHTEyJEjceXKFYnbHD58WLjf3bt3l5uPr776CmpqatDQ0EBUVNTb/EkflYoc+82bNwuPfUlTIVk+++wzqKmpQVtbGy9fvpS4zqNHjzBjxgw0bNgQJiYm0NPTQ61atTBmzBgEBAS81d/4oTx48AALFy5Ely5dYGtrCx0dHRgaGsLZ2RmjR4/G9evXpW47b9484TEFgOzsbPzxxx/w8PCAkZERjIyM4OnpiRUrViA/P7/cvFy+fBn9+/eHtbU1dHV1UatWLUyePBlPnjwBAPj4+EBNTQ0+Pj4y9xMdHY3vvvsOHh4eMDMzg66uLuzt7TF48GCcP39e6nYRERHCv6ekCdD+/fvRvXt31KhRA5qamuWm/aGJ1lUl5s+fL/ydpBFF37bev379OubMmQMfHx9h/W1sbIwGDRpgypQp5dbdb44yFhcXh9mzZ6Nhw4YwMjKCmpoaLly4UIGjIT9F7wMkfTekkTWS65vXlpycHCxfvhwtW7aEhYUF1NTUMG/ePKn7unXrFoYOHSosNzs7O/j6+iI0NFRqfi5cuCBM88KFCygsLMSGDRvQvn17WFtbQ11dvUxe5RkF7sCBA+jbt6+wzjAyMkKtWrXQrl07/Pjjj7h586bMY3Tz5k1MmDABLi4uMDQ0hIGBAerVq4epU6ciLCxM5rbvRBGRirl06VKRsbFxEQCZP35+fmW28/b2LncbJyenotDQUJnpf/7551K3t7KyKrp9+3aRg4NDEYCi0aNHi22/ceNG4foPHjwo6tChg9T9eXp6FqWnp0vNS2JiYlGbNm1k/k3W1tZF169fl7j9nj17irS1tcs9LkFBQWW2S0tLK2rXrl252w0YMEDmsXyfRI9zeHi42PKTJ0+W+z2aOnVqUUFBQZnt8vPzi2rUqFEEoKhLly4y85CXl1dkZWUl17ofk/d17FNTU4v09PSKABT5+vrKzENeXl6RhYVFEYCiXr16SVzn559/LtLU1JSaBzU1taKffvqpwsfhQzh//ny55yGAom+//Vbi9nPnzhWuEx8fX9SkSROp++jVq5dYmYhauHBhkZqamsRtjYyMik6ePCmsh729vaXu599//xWWs7SfcePGFeXl5YltGx4eLlxnw4YNRSNHjhTbVlbalUH0fJH2I3otedt6X570NDQ0ilauXCk1z6NHjy4CUOTg4FDk7+8vPNdEf86fP/+Oj1SpitwHiH43Nm7cKHP/8l7Db926VeTm5iaW7ty5cyXua/369VLrHB0dnaJdu3ZJzI/oeX78+PGiTp06yfyOiJbPm/Lz84sGDhxY7rFr2rSpxLzk5eUVTZkyRea2WlpaRWvXrpV5jN8WB0EglZKTk4MhQ4YgNTUVRkZGmDJlCtq3bw8rKyvk5eUhMjIS/v7+2Ldvn9i2+fn5aNSoEXr37o1mzZqhRo0aKCoqQmRkJA4cOIA9e/YgPDwcffv2RWBgIHR1dcX2sXTpUvz1118AgBo1auC7776Dp6cnsrOzcfToUSxfvhyffvqpXG9uAGDixIm4fv06Ro8ejUGDBsHGxgbPnz/H77//Dn9/f9y8eRMLFy7Er7/+KrZtRkYGvL29ERoaCm1tbYwZMwbdu3eHnZ0dMjIycOnSJSxbtgwJCQno1q0b7t69W2YUoYSEBIwZMwa5ubmwsrLCtGnThE+wsrOz8ezZM1y6dAn79+8XS3vevHm4fPkyAKBnz54YPnw47O3toaurixcvXuDevXs4cuRImSeayiQwMBC9evVCbm4utLS0MHXqVPTp0wcGBga4e/cuFi9ejPDwcKxcuRIGBgb47bffhNtqaGhg9OjR+PXXX3H69GlER0fD1tZWYjpHjx5FYmIiAGDs2LEf5G9Tdm9z7I2MjNC7d2/s3r0b+/fvx6pVqySepwBw8uRJ4VDCw4cPF1v+008/YcGCBQCA1q1bY+zYsWjYsCG0tLTw6NEjrFixAv7+/vj5559hYWGB6dOnv4ej8fby8/NhYGCAHj16oEOHDqhXrx6MjY2RmJiI4OBg/P3334iMjMTixYvh4uKCMWPGSN1X//79ERoaihkzZqBXr14wNzfHo0ePsGDBAoSGhsLPzw/r1q3DpEmTxLbduXMn5syZAwAwMzPD7Nmz4eXlBUDwVmjx4sUYMmQILC0tZf49GzZswPjx4wEArq6umDRpEtzd3aGvr4/w8HCsX78ex44dw/r162FiYoKlS5dK3dfy5ctx//59tGvXDlOmTIGLiwtSUlKUbm6qvn37olmzZgCARo0aAQCmTJmCzz77TLiOmZkZgLev9wHBd8bMzAy9e/eGt7c3nJ2dYWBggNjYWAQEBODvv/9GUlISpk2bhnr16qFDhw5S856eno4BAwYgOzsbP/zwAz755BPo6+sjKCgI1atXf9eHCsDb3Qe8a+PGjUNQUBBGjRqFwYMHC6/hOjo6YusGBgZix44dsLKyKnPvcOzYMSxfvhw5OTkYMWIEnJyc4OnpKTXN2bNn4/79++jduzd8fX3h4OCAhIQEsVYv0qxatQr//fcfAKBt27YYP348ateuDUNDQyQnJ+PBgwc4fvw4kpOTpf7NW7ZsAQB069YNw4cPh4uLC9TU1BAYGIjly5cjODgYEydOhI2NDXr16iVXvhT2XsMrIiVz9uxZiU923pSXl1f0+vXrMr97/PixzH2fPn26SF1dvQhA0b///iu2PD4+vkhfX1/4VCUuLk5i/kSf7pT39AhA0datW8XWyc7OLnJ1dS0CUFStWjWJTzqnTZtWBKDIxMSk6NatWxL/poiIiKLq1av/v707D4/p+v8A/p6YySYimyApklQ0q9hLUXRRitr6iNpiKRJaX0K1gupCW1+KtqTW4GutrTQVUiW2iiUhCIpKUlK1hiAS2T6/P/Kb0xkz984kmZGk83k9zzzPJPeec889c+fO+dxz7rkEgAYNGqS1bMWKFaIMT/fwaMrNzaXHjx9r/a9evXoEgN5++23JdEREd+/elV1uTnK9EC1bthRXOePj43XSZmVlUUBAAAEgKysrSk1N1Vp+5coVcaV71qxZkmV46623xGeYl5dnkv2qCsxZ9z/99JPIe/PmzZJlGDBgAAEgBwcHneP3+PHj4rs+bdo0vemLiopo0KBBovfi3r17xu38M3b79m3Zsj158oRef/11cd4qLCzUWq7ZA6RSqfRetb979y7Vrl2bAFDjxo11lufl5YmeThcXF7p48aLOOhcvXiQXFxfZXpirV6+Kc2xYWJje8x4RUVRUlDg+nt6W5lV+ADRkyBAqLi6WrJ/KRl1uzR4ETeU97xMRZWZmUk5OjmQZ7t+/T40bNyYA1K5dO73rqHsY1N+xlJQUwztnImVtB5ijBwgArVixwqi85NoO+/btE22HFi1a6Cx/uqd3+vTpstuU6wFSj9548cUXJb9jRPp/v7ds2SLKsGzZMr3pcnNzxcgWLy8v2W2UBwdAzKKsW7dOfPmeDnBMoVevXgSAunfvrrNs9uzZYttbtmyRzEOza9jQybNPnz6S+SxevFisd/r0aa1lt2/fJltbWwJA33zzjew+RUdHi8aN5o/erFmzCAA5OzvLptdHpVIZte2KJNUIP3bsmPj/6NGjJdMfPnxYrDdmzBid5eoTvK+vr970N2/eFD9o48aNK/f+VCXmrPv8/HxydXUlANSrVy+96R89ekTVq1cXDeCn9e3bVwzxkGsc37t3j2xsbGR/7KuClJQUUZ9JSUlayzQDoMjISMk8PvroI7He/fv3tZZt2LBBLJM7J3zzzTeyAdDEiRMJAHl4eFBubq5kPgUFBeTp6UkAaOrUqVrLNBu5Tk5O9ODBA8l8KiO5AMgU531jbd++XZTlzp07Oss1A6DPPvus1PmXR1nbAeYIgF555RWD29UMgIxtOxw/flxrmWYA1KhRI50LGU+TC4B8fX0JAE2YMMFg2Z/WvHlzAkC9e/eWXe/8+fOivHv27Cn1dozBkyAwi6LZpb5y5cpy5XX79m1cvnwZqamp4qUennH69Gmd9dU3XTs7O6Nnz56S+ZZmqJO+oTlqzZs3F+/T0tK0lsXHxyMvLw8A0K9fP9ltqIehFBQUIDk5WfxfXZf37t3Djh07jC6zZtoffvjB6OF+lYXmzfMjRoyQXK9t27bw9/fXSaOmHqZz+fJlvTftr1mzRtw0zsPfSpii7lUqlTjm4+Li9N7svWPHDuTk5ADQ/Y4VFBRg165dAIC3335bdpimk5OTGJKUmJgouV5l8uTJE1y9ehXnz58X5zUiEsv1ndvUjD0fpaenay3bu3cvAMDKygqDBw+WzGPQoEGy9a0+D/Xo0UNyaCMAKJVKtGnTBoD859KjRw/UqFFDcnlVY4rzvj45OTnIyMjAuXPnxDGjOQud3DEDyB835mDKdkB5lWbfS9N2kJvkJTQ0FNWqVTN6u09T119sbKwYJmyMv/76SxxLho4/f39/uLm5ATDfuZMDIGZR2rVrBx8fHwDA+PHj0apVK3z55Zc4cuSImD5Uzm+//YbQ0FC4urrC3d0djRo1QnBwsHgtW7YMAPSeFM6ePQsAaNq0KZRK6dvvmjRpAmtra6P2x8/PT3KZi4uLeP/w4UOtZUlJSeJ93bp1tWYLevoVFBQk1r1x44Z4/9Zbb8HJyQkA0Lt3b7zyyiuYP38+kpOTUVRUJFvusLAwAMCRI0fg7e2N9957Dz/++CNu375teKcrWGpqKgDA2toaTZs2lV33xRdfBFAS5Dx9fPXp00d8Rvp+hNX/a9asGUJCQspd7n8DU9W9utGRn5+PLVu26KRdt24dAKB27dp49dVXtZadP39eBO1TpkyR/e4oFArxXdP87lQ2OTk5+PLLLxESEoLq1aujQYMGCAwMFOc1zbqWa/CU9Xyk/ly9vb3FvSpSeajP30/Lzs4WM8UtWbLE4Oei/tzlPpfGjRtLLquKTHHeV7tz5w6ioqLwwgsvoEaNGvD29kZQUJA4Zrp166a1rhQHBwfJz9RcytsOMKXSHGOlaTuov1Pl3aY+6t/vP/74Aw0bNsTw4cOxYcMGZGZmyqbTPP7eeecdg99R9XFjrnMnB0DMoqhUKsTGxoqrwydOnEBUVBTatm0LJycndO3aFevXr9fbgP/kk0/Qrl07bNq0SfLmPrXc3Fyd/927dw8A4O7uLptWqVRqNRbk2NvbSy6zsvrn6/30/qhvrC8tzd4aV1dX/PTTT/D09AQRISEhAZGRkWjRogVcXFzQt29f/Pzzz3rzmT59OoYPHw6FQoFbt25h0aJFYurb4OBgzJgxAzdv3ixTGc1N/dm7uLjI/hgBQJ06dQAARCQ+fzUbGxtxtXvTpk2ixwEomR703LlzAOR7OiyNqer+pZdeEtO7qoMdtTt37uCXX34BAPTv31/nSqkpvjuVSUZGBoKDgxEVFYUzZ84YvHih79ymVtbzkbHnRgCSkyCY43ORC8aqIlPVUXJyMvz8/PDll1/i0qVLWj2E+sgdM+qLaM9SedoBplaaY6w0bQe5Nkp5j+vhw4cjKioKSqUS2dnZWLlyJQYMGIB69eqhYcOGmDRpks6oE6DynTt5FjhmcQICAnD27FnExsYiNjYWBw4cwJUrV5Cbm4vdu3dj9+7dmDdvHuLi4sQJZ+/evfj0008BAD4+Ppg0aRLatWuH+vXrw8HBQTSSNGeGkmLMzGaGflDKS31it7a2Nji8QdPTs5W1b98ef/zxB7Zu3Yq4uDgcPHgQmZmZePDgAbZt24Zt27bhjTfewLZt27QaRyqVCitWrMDEiROxYcMG7Nu3D0lJScjPzxdDKObNm4e1a9fKdvlXJFN8ju+++y6++eYbPHr0CFu2bBFX1tS9P7a2thgwYED5C/svU966VygUGDBgAL744gscPHgQf/31Fzw9PQGUBKPqoYf6hqdoNormzJmDLl26GFXm6tWrG7XeszZ48GCkp6dDoVBg2LBh6N+/P/z9/VGrVi0xE1VxcbE4x5n73FRWmp/L+PHjjb5wINfbXp5hQpWRKc77+fn56NevH+7evQuVSoX3338fPXv2RKNGjeDs7CyOmbS0NDz//PMA5I+ZiqrjsrQDzKE0+2+qtoMp6nzWrFkYNWoU1q1bh7179+Lo0aN4/Pgxrly5gq+//hrffvstvv32W4SHh4s0mt/RdevWGd0TZa4LERwAMYtUrVo19OrVC7169QJQ8hC2Xbt2ITo6GsnJyUhOTsbo0aPx448/AoAY2ubk5ITExETJE+LTV5o1OTs748aNGwZ7NgoLC2XzMQVXV1cAJT9mrq6u5Zpu1NbWFgMHDhSNxbS0NOzcuRMLFy7EpUuXEB8fj6lTp2L+/Pk6aQMCAvD555/j888/R25uLn777TesX78e//vf//Do0SO88847uHLlitmmQy0L9RW2u3fvorCwULYnQv1ZKxQKvSfxoKAgvPjiizh27BhWrlyJsLAw5OXlYePGjQBKhhZWxBXSysqUdT9o0CB88cUXKC4uxoYNGzBp0iQA//QI+fr6omXLljrp1N8doOT+CM2hQlXN77//Lu4/mzJlCmbNmqV3PXOfj9SfjzFXiKWGyWp+Lo8fP67Sn4u5mOK8v2/fPnF1f9GiRRg5cqTe9cx9zJhCadsBmr2YxcXFsnlr9uibSmnaDsaOIimPBg0aICoqClFRUSgoKMDx48exefNmLFmyBHl5eRgzZgxefPFFMYRW8zv69DDLisBD4BhDyXjo4cOHIzExEc2aNQMA/Pzzz6LrXj0c6ZVXXpG9GqQ5xvVp6puhU1JSZJ+Ifvr0abOPQ9Yc068e7mMqPj4+eP/993HixAlx5XDTpk0G09nZ2eG1115DTEwM5syZA6Bk6ITUMLqKoj5p5+fn49SpU7Lrqp+E7evrK3mlWT0ZwsGDB5GWloZt27aJG/N58gNtpqx7f39/8T1Yv349gJLhYOobbqVuTg4MDBT5mfq786ypz2tAyXA/KXLnNVMIDAwEUDI5gtzQnaysLL1Da4CSoXHqXrxff/210vZUVSRTnPcryzFjDobaAZoTYsgFeHfv3i3V5ADGKk3b4VkHFyqVCm3btsWCBQvE+ZSItO6xNGe7oyw4AGJMg0qlQocOHQCUXE1RN0TVJx25sagpKSk4evSo5PLXXnsNQMmPeGxsrOR6MTExpS12qXXt2lXM0jN//nzZk2pZOTo6iivopf0x0Lzx3Bw/JOWh/hwBYMWKFZLrJSYm4vz58zppnta/f384ODiAiLBq1Sox/M3Ly0vnBnxLZ+q6Vwc5p06dwoULF7B+/XrRcJYaemhvby8+l/3794tAqyrS/N7LndsWL15s1nKo67O4uBhr166VXG/t2rWygc1bb70FoKQXWt/kFpbOFOd9Y46Z4uJiLF26tGyFrASk2gHOzs6iR14uwNuwYYNZylWatoPcec/cpH6/GzZsiICAAADAxo0bcfXq1WdeNk0cADGLcujQITFTkD75+fk4cOAAgJLZadQ33Pr6+gIADh8+rPcK5O3btzFo0CDZbYeFhcHOzg4AEBkZqbc7+8CBA8/kh8PT01M80f306dMYPXq07I/hrVu3sHz5cq3/xcfH4++//5ZMk52dLRqH3t7e4v9ZWVn46aefZBsymleHNNNWBq1atRKB3fLly7Fnzx6ddbKzs8XT7q2srBARESGZn4ODg7iSumTJEuzbtw8AMHToUKPGfFsSU9f9O++8I4a1rFu3Tly5bNWqlfjO6zN16lTx2fTv3x9XrlyRXLeoqAjr1683OENSRdDcx9WrV+td5/vvv8f27dvNWo7evXuLnvVPP/0Uly9f1lnn8uXL4j5MKR988IG4ByU8PNxgL0RcXBzOnDlTxlJXPaY47xtzzEyZMgUnT540QYnNo6ztAOCf6cF37Nih93t/4cIFfPzxxyYu8T+MaTs0b95c7/BdU1m7dq3scSP3+z1t2jQAQF5eHvr06SM78+uTJ08QHR0tpm43ObM8XYixSmrGjBlkZWVFHTp0oP/+97+0e/duSk5OpsOHD1NMTAy1atVKPHxr/PjxIt3mzZvF/5977jn67rvv6MiRI/Tbb7/RnDlzqG7duqRQKKhNmzZiPX3mzp0rlnt6etLChQvp+PHjdPDgQfroo4/IxsaGGjRoQLVq1TLqIWqaD4l8mqGHtj18+JCCgoLEOgEBAbRgwQI6dOgQnTp1ihISEmjhwoXUq1cvsra2pubNm2ulDwsLI5VKRW+++SYtWLCAfv31Vzp58iQdOHCAFi1aRP7+/iLvBQsW6JTLy8uLIiMj6YcffqCjR49SUlISxcbG0qhRo8jKykrU9aNHj+Q/VDORq+dTp06RtbW1eFBgZGQkJSQk0IkTJ2jp0qXk4+Mj0k6ePNngto4ePSrWB0AKhYIyMjLMtGeV37Os+1dffVU89FKdzpgH9Go+ANTBwYH+85//0M6dO+nkyZOUmJhIGzZsoHHjxpGHhwcBoLNnz5a1OsymuLhY6xzwzjvv0M8//0zJycm0fft2evvttwkAtW3bVvIBm5r1IEfzQYwJCQk6y9evXy+WOzs70+zZsykxMZESExNp9uzZ5OLiQk5OTuIhjB07dtS7Hc1jx9ramkaMGEE//vgjJScn07Fjx2jr1q304Ycf0vPPP08AKDY2Vit9aR52WRlJfU5q5T3vP3r0iNzd3QkAKZVKGjNmDO3evZuSkpJo48aN4vukeczoq0e5B22aW1nbAUREu3fv1moLLF++nJKTk+nAgQM0ffp0cnR0pIYNG5rkN1xN/SDUkJAQUqlUWm2HQ4cO0ZQpU8QDbpVKJR09elQnD0Pfv6fJfT4AqHbt2hQREUFr1qyhI0eO0MmTJ2nXrl0UGRlJdnZ24rx47do1ybwBkJubG02dOpV++eUXOnXqFB0+fJhWr15N7777Lrm4uBAAevjwocHylgUHQMyiaP5Yy7369Omj8yTxYcOGSa5frVo1WrBggVGNgXHjxknm4+bmRidOnDD6KdLlCYCIiO7evUtdunQxqk46deqklVbzJCb3Gjt2LBUVFektl9zL09OTTp48Kbl/5maonuPj48nR0bFU+y4nODhYpHv99ddNvDdVy7Os+5iYGJ3v8o0bN4wq5/z588nGxsbgsWxtbU2XL18ubTU8E6dOnSJnZ2fJsgcHB9P169clG9amCoCIiGbOnEkKhUJvOezt7Wnnzp3Uvn17AkBdunSR3NbGjRsNHh8AyMrKivbt26eV9t8eABGV77xPVBIEqBvc+l4dO3ak1NRU2Xqs6ADImH3X1w4gkv8Nr1evHp07d84kv+FqmnktW7aMlEql5Hlmw4YNevMwdQBk6OXk5ETx8fF68y4sLKTJkydTtWrVDOZTvXp1evz4scHylgUHQMyi5OTkUFxcHE2YMIFat25N9evXJ1tbW7K1tSUvLy8KDQ2lnTt3SqZfs2YNtW/fnmrUqCF6awYPHkzHjh0jIuMbAzt37qQ33niDXFxcyNbWlho2bEjjxo0TV0ueVQCktnfvXho2bBj5+vqSg4MDKZVKcnFxoZYtW9LYsWMpLi6OCgsLtdLcu3ePtm7dSuHh4dSiRQvy9PQka2trsrOzo0aNGtHQoUPp8OHDOtsqLi6mlJQUmjNnDnXt2pVeeOEFcnJyIqVSSW5ubtShQweaO3cuPXjwQLbM5mZMPd+6dYuioqKoSZMm5OjoSDY2NlS/fn0aOHAgHTp0qFTbmz17ttie1I+YpXiWdZ+dna3VmOvcuXOpypqZmUnTp0+n1q1bk5ubGymVSqpevTo1atSI+vbtS4sXL6bbt2+XKs9n7c8//6Tw8HBq0KABqVQqcnFxoVatWtHcuXNFA/BZBEBERAcPHqRevXqRu7u7OMcOHz6czp8/T0REISEhBIBCQ0Nlt5eVlUVfffUVdezYkdzd3UmlUpG9vT35+PhQjx49aN68eXT16lWddJYQAKmV5byvlpqaSoMGDSIPDw9SqVRUq1Yt6tChAy1dupSKiooM1mNFBkDlbQcQlfRYvvzyy+To6Eh2dnb0wgsv0EcffUR37twhItP8hqs9nVdiYiL169ePPDw8yNramjw9PWnIkCF07tw5yTxMGQD9/vvv9N1331GvXr0oICCAXF1dSalUkrOzM7Vu3Zo++eQTunnzpsFtXLx4kSZOnEhNmzYlZ2dnqlatGtWoUYMCAwNp4MCBtHr1arO2AxREPFUKY4w9LSYmRjxL5Nq1azrPQDK1wYMHY+3atXB2dsbff/8t7mVgjJUoKChAzZo1kZubi2nTphl85hpj/wZeXl74888/ERYWhlWrVlV0cf41eBIExhjT48GDB+J9zZo1zbqt7OxsbN26FUDJzGQc/DCma/v27WJK4tatW1dwaRhjVRkHQIwxpkdKSgoAoE6dOlrPfzCHRYsWiYadegYzxiyN3MxcGRkZiIyMBADUrl0bb7zxxrMqFmPsX0j6MdqMMWZhbt26hbS0NOzfv188y6Fr164m305hYSEyMjLw5MkTHDx4EDNnzgQAdO/evcKfjs1YRfHz88Obb76J7t27IzAwENWrV8etW7eQkJCAxYsXi+exzJ07F0olN18YY2XHZxDGGPt/0dHRWs8acXJyEs8tMKXMzEyd58w4Ojpi/vz5Jt8WY1VFUVERYmNjJR/2aGVlhZkzZxp85hpjjBnCQ+AYY0yDlZUVateujf79+yMxMRE+Pj5m3Z67uzt69uyJI0eOoGHDhmbdFmOVWWxsLMaMGYMmTZqgbt26sLa2Ro0aNeDn54fw8HCcPn0aU6ZMqehiMsb+BXgWOMYYY4wxxpjF4B4gxhhjjDHGmMXgAIgxxhhjjDFmMTgAYowxxhhjjFkMDoAYY4wxxhhjFoMDIMYYY4wxxpjF4ACIMcYYY4wxZjE4AGKMMcYYY4xZDA6AGGOMMcYYYxaDAyDGGGOMMcaYxeAAiDHGGGOMMWYxOABijDHG/p9CoYBCocAnn3yis2zVqlVieUZGxjMvW0UwxT57eXlBoVBg6NChJi2b2v79+0UZ9+/fb5ZtGMsSjxHGqiIOgBhjjJWKZoPz6ZednR3q1auH7t27Y/ny5cjLy6vo4jLGGGNaOABijDFmMnl5ecjMzMTOnTsxcuRINGnSBJcuXaroYlV63HPAGGPPDgdAjDHGyiwiIgJnz54Vr2PHjmHJkiXw9/cHAFy8eBFdunRBbm5uBZe0/IYOHQoiAhHBy8uroovDGGOsjDgAYowxVmbu7u4ICgoSr1atWmHUqFFITk5Gq1atAADp6elYsWJFBZeUMcYYK8EBEGOMMZOzs7PDrFmzxN+7du2qwNIwxhhj/+AAiDHGmFm0bt1avP/zzz/F+4yMDHG/y6pVqwAA27Ztw5tvvgkPDw8olUp07NhRJ7/MzExMmTIFzZo1g7OzM2xtbVG/fn2EhoYiISHBqDKtW7cOHTt2hLOzMxwcHBAUFIQZM2bg/v37BtMae59OcXExNmzYgL59+6J+/fqws7ODq6srQkJCMHz4cOzevRuFhYUA/plQYtiwYSK9t7e3zuQSUrOb7dmzB4MGDYK3tzfs7Ozg6OiIkJAQTJ48GX///bfBfbp37x4++ugj+Pn5wc7ODu7u7njttdewefNmg2lN5e+//0Z0dDTefvtt+Pr6onr16rCxsYGnpyd69uyJH374AcXFxUbnV1xcjGXLluGll16Ci4sLqlevjpCQEHzxxRdGD8Usb70yxio5YowxxkohISGBABAAmjFjhuR6ubm5Yj0/Pz/x//T0dPH/mJgYGjx4sPhb/erQoYNWXsuXLyc7Ozud9TRfI0aMoIKCAr1lKSgooL59+0qmff755yktLU12v1auXCmWp6en691Oeno6NWnSRLacACghIUGnLo1ZX+3Ro0fUu3dv2TQODg4UGxsr+fmcO3eO6tatK5l++PDhRu2zIQ0aNCAAFBYWprOssLCQrKysDO7/66+/Tg8fPtSbv2YdxsfHU5cuXSTz8ff3p+vXr0uWtbz1aor6YoyZn7JU0RJjjDFmpDNnzoj3Hh4eetdZsGABzpw5g/bt2yMiIgKNGjXC/fv3tXpYYmJi8O677wIAgoKCMHr0aDRt2hT29vbi/qK4uDisWLECNWvWxNdff62znQkTJmDr1q0AgBdeeAGTJ09G48aNkZ2djc2bN2PZsmUIDQ0t1/7evHkTbdu2xfXr1wEAr7zyCsLCwuDn5weFQoH09HTs27dPq3elZcuWOHv2LHbs2IFp06YBAOLj43Xqy9vbW7wvKipCjx49kJCQAIVCgf79+6NPnz7w9vZGQUEBjh8/jq+//hpXr15F3759ceTIETRv3lwrv+zsbLzxxhuiNyM0NBRhYWFwd3fHpUuXMG/ePMTExODs2bPlqhNDiAhASV117doVwcHBqFWrFh4+fIi0tDQsW7YMiYmJ2LNnD8aOHYvVq1fL5jdt2jScOHECnTt3RkREBOrVq4dr164hOjoae/bswYULF9CtWzccP34cSqV2E8gU9coYqyIqOgJjjDFWtRjbA9SzZ0+x3meffSb+r9kDBICGDBlCxcXFevO4evUq2dvbix4EqR6eqKgoAkBWVlZ08eJFrWWnT58WvQzNmjXT25OwevVqrTKVpQeoV69eYvns2bMl6+XRo0eUlZVVqrw1zZ07lwCQSqWiuLg4vetkZWVRYGAgAaB27drpLI+MjBTb++KLL3SW5+fnU+fOnbXqxBw9QMXFxXT58mXZ9B9//DEBIIVCQZcuXdJZ/nQv2qhRo/TmM2LECLHOwoULdZabol65B4ixqoEDIMYYY6UiFwA9fvyYjhw5Qj169BDrODo60q1bt8Q6mgGQk5MTPXjwQHJbEydOJADk4eFBubm5kusVFBSQp6cnAaCpU6dqLYuIiBDbS0pKksyja9euZQ6ALly4QAqFggBQz549JbchxdiGc35+vhi2NmHCBNk84+LiRJ6aQUZeXh45OzsTAGrcuDEVFRXpTX/t2jVSqVRmDYCMUVhYSG5ubgSA5s6dq7Nc83isXbs25eTk6M3n4cOHVKtWLQJAAQEBWstMUa9EHAAxVlXwJAiMMcbK7NNPP9W6Wd/e3h4vvfQSYmNjAQCOjo7YunUratWqpTd9jx49UKNGDcn8d+zYIdaztbWVXE+pVKJNmzYAgMTERK1lv/76KwAgODhYdsjS8OHDJZcZEhcXJ4ZzTZgwocz5GHL8+HExbK1fv36y67788svivWadJCcn4969ewCAsLAwWFnpbwo899xz6Ny5c3mLXCrFxcW4fv06Ll68iNTUVKSmpuLChQt47rnnAACnT5+WTd+vXz/Y29vrXebg4CDq7Pz581qTGZiiXhljVQcHQIwxxkyuXr16eP/993H27Fm89tprkus1btxYcll2djb++OMPAMCSJUt0ZkZ7+rVlyxYAwI0bN0QeeXl5Io+WLVvKlln93KKyOHXqFABApVJpzX5naklJSeJ9mzZtZOvDwcFBrKtZJ5r39ZizToxFRFi7di06deoEBwcHeHp6ws/PD8HBweKVkpICALhz545sXqXZn9TUVPHeFPXKGKs6eBIExhhjZRYREYExY8aIv21tbeHq6gpnZ2ej0sutd+vWrTKV6fHjx+L9/fv3Rc+Mu7u7bLratWuXaXvAPw1zFxcX2NjYlDkfQ0xRJ+reH8C8dWKMvLw89OnTx+jnRBmaxro0+5OVlSXem6JeGWNVBwdAjDHGyszd3R1BQUFlTl+tWjXJZUVFReL9+PHjMWLECKPytLa2Fu/VwQ8AKBSKMpSwdMy9Dc062b9/P1xdXY1KpxkYlKZONNc1h1mzZongp0OHDhg7diyaNWuGOnXqwM7OTgzPe/nll3Ho0CGD5Snr/piiXhljVQcHQIwxxiolzUbo48ePyxRoafYw3bx5U3ZdQ8vluLm5AQDu3r2L/Px8rSDMlDTrxNraukx14uLiIt7fvHkTjRo1kly3rD0jxiAiLF++HADQrl077Nu3T/J+JM1eKzmGPkPN/dGsB1PUK2Os6uB7gBhjjFVKtWrVgqenJ4CSiQzK0htha2sLX19fAMCJEydk1zW0XE6zZs0AAAUFBWW6Md7YnqOmTZuK97/88kuptwOUTAahZs46MSQrK0vcQ9OvXz/J4OfRo0e4ePGiUXmWZn80gxxT1CtjrOrgAIgxxlil9dZbbwEA0tLSxCQHpaWehOHs2bNisgJ9YmJiypQ/AHTr1k0EMfPnzy91es0Z7p48eSK5Xrt27UTPxeLFi/HgwYNSb6t58+aiZ2zNmjWSgeVff/1l1mCgsLBQvJe7l2bFihUoKCgwKs/NmzdL3ieUk5ODTZs2AQACAgJQt25dscwU9coYqzo4AGKMMVZpffDBB2JSgfDwcK3ZuvSJi4vDmTNntP43evRoEZyMGjUKOTk5OunWrVuHuLi4MpezUaNG6N27N4CSqbvnzJkjuW5OTo7OkC7NxviVK1ck09ra2mLSpEkASmYg69+/v979UXv48CEWLlyo9T8bGxsMGzYMAJCSkqK3rIWFhRg5ciTy8/Ml8y6vWrVqwcnJCQCwceNGvds6ceIEpk2bZnSeN27cwMSJE/Uui4yMFEPgIiIitJaZol4ZY1UH3wPEGGOs0vL29sbixYsxbNgwZGVloW3bthg8eDC6d++O+vXro7CwEJmZmTh+/Di2bNmCK1euIDY2Vmt67ZCQEIwdOxYLFy5EUlISWrRogQ8//BDBwcHIzs7G5s2bsXTpUrRo0cJggCUnOjoaR48exfXr1zF58mTs3r0bQ4cOhZ+fHxQKBTIyMrB//35s3LgRW7ZsQceOHUXapk2bwtbWFnl5eZg+fTqUSiW8vLzEsDBPT0/Y2dkBACZPnoy9e/di79692LVrFwICAhAeHo42bdrAyckJDx8+xMWLF7F//35s374dtra2eO+997TK+vHHH2PTpk3IzMzEhx9+iJSUFAwZMgTu7u64dOkS5s2bhxMnTqBly5ZmGwZnZWWFgQMHYtGiRUhJSUH79u0xYcIENGzYENnZ2YiLi0N0dDQcHBzg4eGBS5cuGcyzRYsW+P7775Geno7w8HDUq1cP165dw/fff4/4+HgAJXUdHh6uk9YU9coYqyIq6AGsjDHGqqiEhATxtPsZM2aUOn16erpIv3LlSqPSbNy4kRwdHUU6qZeVlRXt27dPJ31+fj716dNHMp23tzelpaXJ7tfKlSvF8vT0dL3lvHLlCgUFBRksZ0JCgk7ayZMnG73+48ePaciQIQa3o943fVJTU6lOnTqS6YYNG2bUPhvSoEEDAkBhYWE6y+7fv09NmjSRLIOLiwsdOHCAOnToQACoQ4cOOnloHo/x8fHUuXNnyfz8/Pzor7/+kixreevVFPXFGDM/HgLHGGOs0gsNDUVGRga++uordOzYEe7u7lCpVLC3t4ePjw969OiBefPmISMjA506ddJJr1KpsHXrVqxZswbt27dHzZo1YW9vD39/f0RFRSE5ORne3t7lLqePjw9SUlKwatUqdOvWDXXr1oW1tTXc3NwQEhKCkSNH4tdff8XLL7+sk/arr77CsmXL0L59e7i4uMhOEW5nZ4fVq1cjKSkJERERCAwMRM2aNaFUKuHk5IQmTZpgxIgR2LJlCy5cuKA3j8DAQJw7dw6TJ0+Gr68vbGxs4Obmhk6dOmH9+vXluifKWDVr1sRvv/2Gzz//HMHBwbC1tYWDgwP8/f0xadIknD59Wm9dSbG2tsauXbsQHR2N1q1bw8nJCfb29ggODsbMmTNx8uRJeHh4SKY3Rb0yxio/BZGZJ/lnjDHGGGOMsUqCe4AYY4wxxhhjFoMDIMYYY4wxxpjF4ACIMcYYY4wxZjE4AGKMMcYYY4xZDA6AGGOMMcYYYxaDAyDGGGOMMcaYxeAAiDHGGGOMMWYxOABijDHGGGOMWQwOgBhjjDHGGGMWgwMgxhhjjDHGmMXgAIgxxhhjjDFmMTgAYowxxhhjjFkMDoAYY4wxxhhjFoMDIMYYY4wxxpjF4ACIMcYYY4wxZjE4AGKMMcYYY4xZDA6AGGOMMcYYYxaDAyDGGGOMMcaYxeAAiDHGGGOMMWYxOABijDHGGGOMWQwOgBhjjDHGGGMWgwMgxhhjjDHGmMXgAIgxxhhjjDFmMTgAYowxxhhjjFkMDoAYY4wxxhhjFuP/ANbDgUW1FG0dAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "preds_output = trainer.predict(emotions_encoded[\"test\"])\n",
    "y_preds = np.argmax(preds_output.predictions, axis=-1)\n",
    "y_true = emotions_encoded[\"test\"]['label']\n",
    "plot_confusion_matrix(y_preds, y_true, labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d074921b",
   "metadata": {},
   "source": [
    "# result analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "f6ae35ee",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-16T03:43:03.531596Z",
     "start_time": "2023-04-16T03:43:03.526416Z"
    }
   },
   "outputs": [],
   "source": [
    "from torch.nn.functional import cross_entropy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "a3a4f845",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-16T03:43:04.266106Z",
     "start_time": "2023-04-16T03:43:04.255609Z"
    }
   },
   "outputs": [],
   "source": [
    "def forward_pass_with_label(batch):\n",
    "    # Place all input tensors on the same device as the model\n",
    "    inputs = {k:v.to(device) for k,v in batch.items() \n",
    "              if k in tokenizer.model_input_names}\n",
    "\n",
    "    with torch.no_grad():\n",
    "        output = model(**inputs)\n",
    "        pred_label = torch.argmax(output.logits, axis=-1)\n",
    "        loss = cross_entropy(output.logits, batch[\"label\"].to(device), \n",
    "                             reduction=\"none\")\n",
    "\n",
    "    # Place outputs on CPU for compatibility with other dataset columns   \n",
    "    return {\"loss\": loss.cpu().numpy(), \n",
    "            \"predicted_label\": pred_label.cpu().numpy()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "9896aa50",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-16T03:43:08.397980Z",
     "start_time": "2023-04-16T03:43:05.707592Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map:   0%|          | 0/2000 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "emotions_encoded[\"validation\"] = emotions_encoded[\"validation\"].map(\n",
    "    forward_pass_with_label, batched=True, batch_size=16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "1fcb1382",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-16T03:43:45.676388Z",
     "start_time": "2023-04-16T03:43:45.669034Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dataset({\n",
       "    features: ['text', 'label', 'input_ids', 'attention_mask', 'loss', 'predicted_label'],\n",
       "    num_rows: 2000\n",
       "})"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emotions_encoded['validation']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "bc26444f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-16T03:43:46.906361Z",
     "start_time": "2023-04-16T03:43:46.900193Z"
    }
   },
   "outputs": [],
   "source": [
    "selected_cols = ['text', 'label', 'predicted_label', 'loss']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "7a29b09d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-16T03:44:26.613594Z",
     "start_time": "2023-04-16T03:44:26.580216Z"
    }
   },
   "outputs": [],
   "source": [
    "valid_df = pd.DataFrame.from_dict({'text': emotions_encoded[\"validation\"]['text'], \n",
    "                                   'label': emotions_encoded['validation']['label'].numpy(), \n",
    "                                   'pred_label': emotions_encoded['validation']['predicted_label'].numpy(), \n",
    "                                   'loss': emotions_encoded[\"validation\"]['loss'].numpy()})\n",
    "valid_df['label'] = valid_df['label'].apply(lambda x: labels[x])\n",
    "valid_df['pred_label'] = valid_df['pred_label'].apply(lambda x: labels[x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "e92353db",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-16T03:44:29.373252Z",
     "start_time": "2023-04-16T03:44:29.346816Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>label</th>\n",
       "      <th>pred_label</th>\n",
       "      <th>loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>im feeling quite sad and sorry for myself but ...</td>\n",
       "      <td>sadness</td>\n",
       "      <td>sadness</td>\n",
       "      <td>0.010114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>i feel like i am still looking at a blank canv...</td>\n",
       "      <td>sadness</td>\n",
       "      <td>sadness</td>\n",
       "      <td>0.009351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>i feel like a faithful servant</td>\n",
       "      <td>love</td>\n",
       "      <td>love</td>\n",
       "      <td>0.417078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>i am just feeling cranky and blue</td>\n",
       "      <td>anger</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.022240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>i can have for a treat or if i am feeling festive</td>\n",
       "      <td>joy</td>\n",
       "      <td>joy</td>\n",
       "      <td>0.009649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>im having ssa examination tomorrow in the morn...</td>\n",
       "      <td>sadness</td>\n",
       "      <td>sadness</td>\n",
       "      <td>0.013275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>i constantly worry about their fight against n...</td>\n",
       "      <td>joy</td>\n",
       "      <td>joy</td>\n",
       "      <td>0.011533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>i feel its important to share this info for th...</td>\n",
       "      <td>joy</td>\n",
       "      <td>joy</td>\n",
       "      <td>0.010809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>i truly feel that if you are passionate enough...</td>\n",
       "      <td>joy</td>\n",
       "      <td>joy</td>\n",
       "      <td>0.406471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>i feel like i just wanna buy any cute make up ...</td>\n",
       "      <td>joy</td>\n",
       "      <td>joy</td>\n",
       "      <td>0.012833</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2000 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   text    label pred_label  \\\n",
       "0     im feeling quite sad and sorry for myself but ...  sadness    sadness   \n",
       "1     i feel like i am still looking at a blank canv...  sadness    sadness   \n",
       "2                        i feel like a faithful servant     love       love   \n",
       "3                     i am just feeling cranky and blue    anger      anger   \n",
       "4     i can have for a treat or if i am feeling festive      joy        joy   \n",
       "...                                                 ...      ...        ...   \n",
       "1995  im having ssa examination tomorrow in the morn...  sadness    sadness   \n",
       "1996  i constantly worry about their fight against n...      joy        joy   \n",
       "1997  i feel its important to share this info for th...      joy        joy   \n",
       "1998  i truly feel that if you are passionate enough...      joy        joy   \n",
       "1999  i feel like i just wanna buy any cute make up ...      joy        joy   \n",
       "\n",
       "          loss  \n",
       "0     0.010114  \n",
       "1     0.009351  \n",
       "2     0.417078  \n",
       "3     0.022240  \n",
       "4     0.009649  \n",
       "...        ...  \n",
       "1995  0.013275  \n",
       "1996  0.011533  \n",
       "1997  0.010809  \n",
       "1998  0.406471  \n",
       "1999  0.012833  \n",
       "\n",
       "[2000 rows x 4 columns]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "valid_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "2ecdbdc5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-16T03:44:50.159137Z",
     "start_time": "2023-04-16T03:44:50.128737Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>label</th>\n",
       "      <th>pred_label</th>\n",
       "      <th>loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>i know what it feels like he stressed glaring ...</td>\n",
       "      <td>anger</td>\n",
       "      <td>sadness</td>\n",
       "      <td>1.297371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>i feel as if i am the beloved preparing hersel...</td>\n",
       "      <td>joy</td>\n",
       "      <td>love</td>\n",
       "      <td>1.196154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>i am feeling very blessed today that they shar...</td>\n",
       "      <td>joy</td>\n",
       "      <td>love</td>\n",
       "      <td>0.952934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>i didn t feel accepted</td>\n",
       "      <td>joy</td>\n",
       "      <td>love</td>\n",
       "      <td>0.990618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>i feel that this is important in itself the fa...</td>\n",
       "      <td>joy</td>\n",
       "      <td>sadness</td>\n",
       "      <td>0.784434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1963</th>\n",
       "      <td>i called myself pro life and voted for perry w...</td>\n",
       "      <td>joy</td>\n",
       "      <td>sadness</td>\n",
       "      <td>6.134463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1964</th>\n",
       "      <td>i feel vaguely cheated and a little amused</td>\n",
       "      <td>joy</td>\n",
       "      <td>anger</td>\n",
       "      <td>3.961156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981</th>\n",
       "      <td>i spent a lot of time feeling overwhelmed with...</td>\n",
       "      <td>fear</td>\n",
       "      <td>surprise</td>\n",
       "      <td>0.957287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990</th>\n",
       "      <td>i just feel too overwhelmed i can t see the fo...</td>\n",
       "      <td>fear</td>\n",
       "      <td>surprise</td>\n",
       "      <td>0.980824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993</th>\n",
       "      <td>i feel so tortured by it</td>\n",
       "      <td>anger</td>\n",
       "      <td>fear</td>\n",
       "      <td>0.901335</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>135 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   text  label pred_label  \\\n",
       "17    i know what it feels like he stressed glaring ...  anger    sadness   \n",
       "27    i feel as if i am the beloved preparing hersel...    joy       love   \n",
       "35    i am feeling very blessed today that they shar...    joy       love   \n",
       "55                               i didn t feel accepted    joy       love   \n",
       "72    i feel that this is important in itself the fa...    joy    sadness   \n",
       "...                                                 ...    ...        ...   \n",
       "1963  i called myself pro life and voted for perry w...    joy    sadness   \n",
       "1964         i feel vaguely cheated and a little amused    joy      anger   \n",
       "1981  i spent a lot of time feeling overwhelmed with...   fear   surprise   \n",
       "1990  i just feel too overwhelmed i can t see the fo...   fear   surprise   \n",
       "1993                           i feel so tortured by it  anger       fear   \n",
       "\n",
       "          loss  \n",
       "17    1.297371  \n",
       "27    1.196154  \n",
       "35    0.952934  \n",
       "55    0.990618  \n",
       "72    0.784434  \n",
       "...        ...  \n",
       "1963  6.134463  \n",
       "1964  3.961156  \n",
       "1981  0.957287  \n",
       "1990  0.980824  \n",
       "1993  0.901335  \n",
       "\n",
       "[135 rows x 4 columns]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "valid_df[valid_df['label'] != valid_df['pred_label']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "cf829951",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-16T03:45:01.576594Z",
     "start_time": "2023-04-16T03:45:01.566322Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9325"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1-135/2000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "be8a8577",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-16T03:45:12.589320Z",
     "start_time": "2023-04-16T03:45:12.573883Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "joy         44\n",
       "fear        25\n",
       "anger       19\n",
       "love        17\n",
       "sadness     16\n",
       "surprise    14\n",
       "Name: label, dtype: int64"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# most labels incorrectly\n",
    "valid_df[valid_df['label'] != valid_df['pred_label']].label.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "3b2e8310",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-16T03:45:58.466573Z",
     "start_time": "2023-04-16T03:45:58.444953Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>label</th>\n",
       "      <th>pred_label</th>\n",
       "      <th>loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1963</th>\n",
       "      <td>i called myself pro life and voted for perry w...</td>\n",
       "      <td>joy</td>\n",
       "      <td>sadness</td>\n",
       "      <td>6.134463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950</th>\n",
       "      <td>i as representative of everything thats wrong ...</td>\n",
       "      <td>surprise</td>\n",
       "      <td>sadness</td>\n",
       "      <td>6.044109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>882</th>\n",
       "      <td>i feel badly about reneging on my commitment t...</td>\n",
       "      <td>love</td>\n",
       "      <td>sadness</td>\n",
       "      <td>6.040215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1870</th>\n",
       "      <td>i guess i feel betrayed because i admired him ...</td>\n",
       "      <td>joy</td>\n",
       "      <td>sadness</td>\n",
       "      <td>5.845109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1111</th>\n",
       "      <td>im lazy my characters fall into categories of ...</td>\n",
       "      <td>joy</td>\n",
       "      <td>fear</td>\n",
       "      <td>5.429384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1500</th>\n",
       "      <td>i guess we would naturally feel a sense of lon...</td>\n",
       "      <td>anger</td>\n",
       "      <td>sadness</td>\n",
       "      <td>5.025256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1509</th>\n",
       "      <td>i guess this is a memoir so it feels like that...</td>\n",
       "      <td>joy</td>\n",
       "      <td>fear</td>\n",
       "      <td>4.937392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>318</th>\n",
       "      <td>i felt ashamed of these feelings and was scare...</td>\n",
       "      <td>fear</td>\n",
       "      <td>sadness</td>\n",
       "      <td>4.877355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1836</th>\n",
       "      <td>i got a very nasty electrical shock when i was...</td>\n",
       "      <td>fear</td>\n",
       "      <td>anger</td>\n",
       "      <td>4.630801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1683</th>\n",
       "      <td>i had applied for a job and they had assured m...</td>\n",
       "      <td>anger</td>\n",
       "      <td>joy</td>\n",
       "      <td>4.392582</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   text     label pred_label  \\\n",
       "1963  i called myself pro life and voted for perry w...       joy    sadness   \n",
       "1950  i as representative of everything thats wrong ...  surprise    sadness   \n",
       "882   i feel badly about reneging on my commitment t...      love    sadness   \n",
       "1870  i guess i feel betrayed because i admired him ...       joy    sadness   \n",
       "1111  im lazy my characters fall into categories of ...       joy       fear   \n",
       "1500  i guess we would naturally feel a sense of lon...     anger    sadness   \n",
       "1509  i guess this is a memoir so it feels like that...       joy       fear   \n",
       "318   i felt ashamed of these feelings and was scare...      fear    sadness   \n",
       "1836  i got a very nasty electrical shock when i was...      fear      anger   \n",
       "1683  i had applied for a job and they had assured m...     anger        joy   \n",
       "\n",
       "          loss  \n",
       "1963  6.134463  \n",
       "1950  6.044109  \n",
       "882   6.040215  \n",
       "1870  5.845109  \n",
       "1111  5.429384  \n",
       "1500  5.025256  \n",
       "1509  4.937392  \n",
       "318   4.877355  \n",
       "1836  4.630801  \n",
       "1683  4.392582  "
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# \n",
    "valid_df.sort_values('loss', ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "9488b2c4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-16T03:46:22.464663Z",
     "start_time": "2023-04-16T03:46:22.453864Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'i feel badly about reneging on my commitment to bring donuts to the faithful at holy family catholic church in columbus ohio'"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# mislabeld\n",
    "valid_df.iloc[882].text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "32187878",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-16T03:47:14.991125Z",
     "start_time": "2023-04-16T03:47:14.967649Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>label</th>\n",
       "      <th>pred_label</th>\n",
       "      <th>loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>i and feel quite ungrateful for it but i m loo...</td>\n",
       "      <td>sadness</td>\n",
       "      <td>sadness</td>\n",
       "      <td>0.008000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1531</th>\n",
       "      <td>i forgive stanley hes not so quick to forgive ...</td>\n",
       "      <td>sadness</td>\n",
       "      <td>sadness</td>\n",
       "      <td>0.008075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1873</th>\n",
       "      <td>i feel practically virtuous this month i have ...</td>\n",
       "      <td>joy</td>\n",
       "      <td>joy</td>\n",
       "      <td>0.008261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>i feel try to tell me im ungrateful tell me im...</td>\n",
       "      <td>sadness</td>\n",
       "      <td>sadness</td>\n",
       "      <td>0.008272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1310</th>\n",
       "      <td>i feel like an ungrateful asshole</td>\n",
       "      <td>sadness</td>\n",
       "      <td>sadness</td>\n",
       "      <td>0.008308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>866</th>\n",
       "      <td>i feel quite jaded and unenthusiastic about li...</td>\n",
       "      <td>sadness</td>\n",
       "      <td>sadness</td>\n",
       "      <td>0.008327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1502</th>\n",
       "      <td>i feel ungrateful for stupid shit like</td>\n",
       "      <td>sadness</td>\n",
       "      <td>sadness</td>\n",
       "      <td>0.008341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>i leave the meeting feeling more than a little...</td>\n",
       "      <td>sadness</td>\n",
       "      <td>sadness</td>\n",
       "      <td>0.008458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1656</th>\n",
       "      <td>im feeling very jaded and uncertain about love...</td>\n",
       "      <td>sadness</td>\n",
       "      <td>sadness</td>\n",
       "      <td>0.008484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1466</th>\n",
       "      <td>i feel so ungrateful to be wishing this pregna...</td>\n",
       "      <td>sadness</td>\n",
       "      <td>sadness</td>\n",
       "      <td>0.008497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>625</th>\n",
       "      <td>i feel unwelcome in this town as if my time he...</td>\n",
       "      <td>sadness</td>\n",
       "      <td>sadness</td>\n",
       "      <td>0.008502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1041</th>\n",
       "      <td>i suppose it all goes along with feeling unwel...</td>\n",
       "      <td>sadness</td>\n",
       "      <td>sadness</td>\n",
       "      <td>0.008524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1147</th>\n",
       "      <td>i type i feel bouncy and excited to get out my...</td>\n",
       "      <td>joy</td>\n",
       "      <td>joy</td>\n",
       "      <td>0.008558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>783</th>\n",
       "      <td>i feel like i deserve to be broke with how fri...</td>\n",
       "      <td>sadness</td>\n",
       "      <td>sadness</td>\n",
       "      <td>0.008572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1120</th>\n",
       "      <td>i am feeling a little disheartened</td>\n",
       "      <td>sadness</td>\n",
       "      <td>sadness</td>\n",
       "      <td>0.008603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>i feel like im an unwelcome presence whenever ...</td>\n",
       "      <td>sadness</td>\n",
       "      <td>sadness</td>\n",
       "      <td>0.008625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1368</th>\n",
       "      <td>i started this blog with pure intentions i mus...</td>\n",
       "      <td>sadness</td>\n",
       "      <td>sadness</td>\n",
       "      <td>0.008637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1263</th>\n",
       "      <td>i feel this way about blake lively</td>\n",
       "      <td>joy</td>\n",
       "      <td>joy</td>\n",
       "      <td>0.008638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>i have no extra money im worried all of the ti...</td>\n",
       "      <td>sadness</td>\n",
       "      <td>sadness</td>\n",
       "      <td>0.008652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>323</th>\n",
       "      <td>im starting to feel unwelcome in there</td>\n",
       "      <td>sadness</td>\n",
       "      <td>sadness</td>\n",
       "      <td>0.008653</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   text    label pred_label  \\\n",
       "133   i and feel quite ungrateful for it but i m loo...  sadness    sadness   \n",
       "1531  i forgive stanley hes not so quick to forgive ...  sadness    sadness   \n",
       "1873  i feel practically virtuous this month i have ...      joy        joy   \n",
       "21    i feel try to tell me im ungrateful tell me im...  sadness    sadness   \n",
       "1310                  i feel like an ungrateful asshole  sadness    sadness   \n",
       "866   i feel quite jaded and unenthusiastic about li...  sadness    sadness   \n",
       "1502             i feel ungrateful for stupid shit like  sadness    sadness   \n",
       "189   i leave the meeting feeling more than a little...  sadness    sadness   \n",
       "1656  im feeling very jaded and uncertain about love...  sadness    sadness   \n",
       "1466  i feel so ungrateful to be wishing this pregna...  sadness    sadness   \n",
       "625   i feel unwelcome in this town as if my time he...  sadness    sadness   \n",
       "1041  i suppose it all goes along with feeling unwel...  sadness    sadness   \n",
       "1147  i type i feel bouncy and excited to get out my...      joy        joy   \n",
       "783   i feel like i deserve to be broke with how fri...  sadness    sadness   \n",
       "1120                 i am feeling a little disheartened  sadness    sadness   \n",
       "76    i feel like im an unwelcome presence whenever ...  sadness    sadness   \n",
       "1368  i started this blog with pure intentions i mus...  sadness    sadness   \n",
       "1263                 i feel this way about blake lively      joy        joy   \n",
       "69    i have no extra money im worried all of the ti...  sadness    sadness   \n",
       "323              im starting to feel unwelcome in there  sadness    sadness   \n",
       "\n",
       "          loss  \n",
       "133   0.008000  \n",
       "1531  0.008075  \n",
       "1873  0.008261  \n",
       "21    0.008272  \n",
       "1310  0.008308  \n",
       "866   0.008327  \n",
       "1502  0.008341  \n",
       "189   0.008458  \n",
       "1656  0.008484  \n",
       "1466  0.008497  \n",
       "625   0.008502  \n",
       "1041  0.008524  \n",
       "1147  0.008558  \n",
       "783   0.008572  \n",
       "1120  0.008603  \n",
       "76    0.008625  \n",
       "1368  0.008637  \n",
       "1263  0.008638  \n",
       "69    0.008652  \n",
       "323   0.008653  "
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# less loss means more confident\n",
    "# sadness/joy\n",
    "valid_df.sort_values('loss', ascending=True).head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1bcb6c01",
   "metadata": {},
   "source": [
    "# to huggingface hub"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "d59b2217",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-16T03:47:54.011233Z",
     "start_time": "2023-04-16T03:47:42.398640Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "To https://huggingface.co/lanchunhui/distilbert-base-uncased_emotion_ft_0416\n",
      "   165d1a1..fd9f9fa  main -> main\n",
      "\n"
     ]
    }
   ],
   "source": [
    "trainer.push_to_hub(commit_message=\"Training completed!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "816ef003",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-16T03:48:55.344671Z",
     "start_time": "2023-04-16T03:48:36.803535Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3a60e5d05b6b4ac6926d2f1c9e6acb44",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading (…)lve/main/config.json:   0%|          | 0.00/888 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2a36e54bc0cc49e2ad540c5442d0ff4d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading pytorch_model.bin:   0%|          | 0.00/268M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "55c1aa7d9802421fb9a675927f39314c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading (…)okenizer_config.json:   0%|          | 0.00/320 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5c6b32f1424a47aeab58102212d35d1b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading (…)solve/main/vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "01b4ef88157b462086e1a53cf150a7d2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/712k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4c9b2ab4ee724cccaf3b5ed9988139ba",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/125 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#hide_output\n",
    "from transformers import pipeline\n",
    "\n",
    "# Change `transformersbook` to your Hub username\n",
    "model_id = \"lanchunhui/distilbert-base-uncased_emotion_ft_0416\"\n",
    "classifier = pipeline(\"text-classification\", model=model_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "52b03b94",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-16T03:49:36.316642Z",
     "start_time": "2023-04-16T03:49:36.285723Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/whaow/anaconda3/lib/python3.10/site-packages/transformers/pipelines/text_classification.py:104: UserWarning: `return_all_scores` is now deprecated,  if want a similar funcionality use `top_k=None` instead of `return_all_scores=True` or `top_k=1` instead of `return_all_scores=False`.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[[{'label': 'LABEL_0', 'score': 0.2951389253139496},\n",
       "  {'label': 'LABEL_1', 'score': 0.08960109949111938},\n",
       "  {'label': 'LABEL_2', 'score': 0.017728755250573158},\n",
       "  {'label': 'LABEL_3', 'score': 0.40038371086120605},\n",
       "  {'label': 'LABEL_4', 'score': 0.175038143992424},\n",
       "  {'label': 'LABEL_5', 'score': 0.022109463810920715}]]"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# custom_tweet = \"I saw a movie today and it was really good.\"\n",
    "custom_tweet = \"I saw a movie today and it suck.\"\n",
    "preds = classifier(custom_tweet, return_all_scores=True)\n",
    "preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "ec3c77ca",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-16T03:49:19.633553Z",
     "start_time": "2023-04-16T03:49:19.623397Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['sadness', 'joy', 'love', 'anger', 'fear', 'surprise']"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "4b21aaec",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-16T03:49:38.751526Z",
     "start_time": "2023-04-16T03:49:38.482225Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1280x960 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "preds_df = pd.DataFrame(preds[0])\n",
    "plt.bar(labels, 100 * preds_df[\"score\"], color='C0')\n",
    "plt.title(f'\"{custom_tweet}\"')\n",
    "plt.ylabel(\"Class probability (%)\")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {
    "height": "calc(100% - 180px)",
    "left": "10px",
    "top": "150px",
    "width": "336px"
   },
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
